From c7c616b53648ebcd77f1d421c987818da403f152 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sun, 10 Oct 2021 16:08:36 -0400 Subject: [PATCH 01/45] modifies behavior_mp_env to allow planning with HLA's * modifies gitignore to ignore json files produced by runs --- .gitignore | 3 +++ igibson/envs/behavior_mp_env.py | 4 +++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 5c81e93cd..72eb7d417 100644 --- a/.gitignore +++ b/.gitignore @@ -80,3 +80,6 @@ igibson/utils/data_utils/mesh_decimation/final_videos # libcryptopp igibson/render/mesh_renderer/libcryptopp.so.8.6 + +# igibson run json files +*my_igibson_run.json \ No newline at end of file diff --git a/igibson/envs/behavior_mp_env.py b/igibson/envs/behavior_mp_env.py index 52c0063c5..92c499a55 100644 --- a/igibson/envs/behavior_mp_env.py +++ b/igibson/envs/behavior_mp_env.py @@ -512,7 +512,9 @@ def navigate_to_obj(self, obj, use_motion_planning=False): return False def reset(self, resample_objects=False): - obs = super(BehaviorMPEnv, self).reset(resample_objects=resample_objects) + # NOTE: The below line used to be super(BehaviorMPEnv, self, resample_objects=resample_objects).reset() + # However, this causes an error because the super call only expects 2 args. + obs = super(BehaviorMPEnv, self).reset() self.obj_in_hand = None return obs From 1122a45d9dffc400254dbff919ab21cb832317f7 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sun, 10 Oct 2021 18:29:11 -0400 Subject: [PATCH 02/45] modifies behavior_mp_env to reveal action_exec_status --- igibson/envs/behavior_mp_env.py | 179 +++++++++++++++++++++++++++++++- 1 file changed, 177 insertions(+), 2 deletions(-) diff --git a/igibson/envs/behavior_mp_env.py b/igibson/envs/behavior_mp_env.py index 92c499a55..f046b2c57 100644 --- a/igibson/envs/behavior_mp_env.py +++ b/igibson/envs/behavior_mp_env.py @@ -286,6 +286,181 @@ def step(self, action): logging.debug("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) return state, reward, done, info + def step_with_exec_info(self, action): + """ + Same as the above step method, but returns an additional param (action_exec_status) + that is True if the high-level action execution succeeded and False otherwise + """ + obj_list_id = int(action) % self.num_objects + action_primitive = int(action) // self.num_objects + action_exec_status = False + + obj = self.task_relevant_objects[obj_list_id] + if not (isinstance(obj, BRBody) or isinstance(obj, BRHand) or isinstance(obj, BREye)): + if action_primitive == ActionPrimitives.NAVIGATE_TO: + if self.navigate_to_obj(obj, use_motion_planning=self.use_motion_planning): + print("PRIMITIVE: navigate to {} success".format(obj.name)) + action_exec_status = True + else: + print("PRIMITIVE: navigate to {} fail".format(obj.name)) + + elif action_primitive == ActionPrimitives.GRASP: + if self.obj_in_hand is None: + if isinstance(obj, URDFObject) and hasattr(obj, "states") and object_states.AABB in obj.states: + lo, hi = obj.states[object_states.AABB].get_value() + volume = get_aabb_volume(lo, hi) + if ( + volume < 0.2 * 0.2 * 0.2 and not obj.main_body_is_fixed + ): # say we can only grasp small objects + if ( + np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) + < 2 + ): + self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) + print( + "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) + ) + action_exec_status = True + else: + print("PRIMITIVE: grasp {} fail, too far".format(obj.name)) + else: + print("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) + elif action_primitive == ActionPrimitives.PLACE_ONTOP: + if self.obj_in_hand is not None and self.obj_in_hand != obj: + print("PRIMITIVE:attempt to place {} ontop {}".format(self.obj_in_hand.name, obj.name)) + + if isinstance(obj, URDFObject): + if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + state = p.saveState() + result = sample_kinematics( + "onTop", + self.obj_in_hand, + obj, + True, + use_ray_casting_method=True, + max_trials=20, + skip_falling=True, + ) + if result: + print( + "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + ) + action_exec_status = True + pos = self.obj_in_hand.get_position() + orn = self.obj_in_hand.get_orientation() + self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + else: + p.removeState(state) + print( + "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + self.obj_in_hand.name, obj.name + ) + ) + + else: + print( + "PRIMITIVE: place {} ontop {} fail, too far".format(self.obj_in_hand.name, obj.name) + ) + else: + state = p.saveState() + result = sample_kinematics( + "onFloor", + self.obj_in_hand, + obj, + True, + use_ray_casting_method=True, + max_trials=20, + skip_falling=True, + ) + if result: + print( + "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + ) + action_exec_status = True + pos = self.obj_in_hand.get_position() + orn = self.obj_in_hand.get_orientation() + self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + else: + print( + "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + self.obj_in_hand.name, obj.name + ) + ) + p.removeState(state) + + elif action_primitive == ActionPrimitives.PLACE_INSIDE: + if self.obj_in_hand is not None and self.obj_in_hand != obj and isinstance(obj, URDFObject): + print("PRIMITIVE:attempt to place {} inside {}".format(self.obj_in_hand.name, obj.name)) + if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + if ( + hasattr(obj, "states") + and object_states.Open in obj.states + and obj.states[object_states.Open].get_value() + ) or (hasattr(obj, "states") and not object_states.Open in obj.states): + state = p.saveState() + result = sample_kinematics( + "inside", + self.obj_in_hand, + obj, + True, + use_ray_casting_method=True, + max_trials=20, + skip_falling=True, + ) + if result: + print( + "PRIMITIVE: place {} inside {} success".format(self.obj_in_hand.name, obj.name) + ) + action_exec_status = True + pos = self.obj_in_hand.get_position() + orn = self.obj_in_hand.get_orientation() + self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + else: + print( + "PRIMITIVE: place {} inside {} fail, sampling fail".format( + self.obj_in_hand.name, obj.name + ) + ) + p.removeState(state) + else: + print( + "PRIMITIVE: place {} inside {} fail, need open not open".format( + self.obj_in_hand.name, obj.name + ) + ) + else: + print( + "PRIMITIVE: place {} inside {} fail, too far".format(self.obj_in_hand.name, obj.name) + ) + elif action_primitive == ActionPrimitives.OPEN: + if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + if hasattr(obj, "states") and object_states.Open in obj.states: + action_exec_status = True + print("PRIMITIVE open succeeded") + obj.states[object_states.Open].set_value(True) + else: + print("PRIMITIVE open failed, cannot be opened") + else: + print("PRIMITIVE open failed, too far") + + elif action_primitive == ActionPrimitives.CLOSE: + if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + if hasattr(obj, "states") and object_states.Open in obj.states: + action_exec_status = True + obj.states[object_states.Open].set_value(False) + print("PRIMITIVE close succeeded") + else: + print("PRIMITIVE close failed, cannot be opened") + else: + print("PRIMITIVE close failed, too far") + + else: + print(f"Attempted to execute a High Level Action whose target was the robot's body! This is incorrect, please pass a sensible high level action argument!") + + state, reward, done, info = super(BehaviorMPEnv, self).step(np.zeros(17)) + print("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) + return state, reward, done, info, action_exec_status + def grasp_obj(self, obj, use_motion_planning=False): if use_motion_planning: x, y, _ = obj.get_position() @@ -451,7 +626,7 @@ def navigate_to_obj(self, obj, use_motion_planning=False): distance_to_try = [0.6, 1.2, 1.8, 2.4] obj_pos = obj.get_position() for distance in distance_to_try: - for _ in range(20): + for _ in range(40): yaw = np.random.uniform(-np.pi, np.pi) pos = [ obj_pos[0] + distance * np.cos(yaw), @@ -473,7 +648,7 @@ def navigate_to_obj(self, obj, use_motion_planning=False): if valid_position is not None: break else: - for _ in range(60): + for _ in range(120): _, pos = obj.scene.get_random_point_by_room_instance(obj.room_instance) yaw = np.random.uniform(-np.pi, np.pi) orn = [0, 0, yaw] From fb82c73c47eb7536ebc69fca34df472eaf8ddc67 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Mon, 11 Oct 2021 17:38:05 -0400 Subject: [PATCH 03/45] includes new tamp env for behavior to integrate with sample-based TAMP approaches * new version of navigateTo primitive implemented to take in (x,y,theta) * modifying other High Level Actions is ToDo! --- igibson/envs/behavior_tamp_env.py | 741 ++++++++++++++++++++++++++++++ 1 file changed, 741 insertions(+) create mode 100644 igibson/envs/behavior_tamp_env.py diff --git a/igibson/envs/behavior_tamp_env.py b/igibson/envs/behavior_tamp_env.py new file mode 100644 index 000000000..4e0b713dc --- /dev/null +++ b/igibson/envs/behavior_tamp_env.py @@ -0,0 +1,741 @@ +import argparse +import logging +import time +from enum import IntEnum + +import gym.spaces +import numpy as np +import pybullet as p + +from igibson import object_states +from igibson.envs.behavior_env import BehaviorEnv +from igibson.external.pybullet_tools.utils import CIRCULAR_LIMITS +from igibson.object_states.on_floor import RoomFloor +from igibson.object_states.utils import sample_kinematics +from igibson.objects.articulated_object import URDFObject +from igibson.robots.behavior_robot import BRBody, BREye, BRHand +from igibson.utils.behavior_robot_planning_utils import dry_run_base_plan, plan_base_motion_br, plan_hand_motion_br + +NUM_ACTIONS = 6 +MAX_ACTION_CONT_PARAMS = 2 + + +class ActionPrimitives(IntEnum): + NAVIGATE_TO = 0 + GRASP = 1 + PLACE_ONTOP = 2 + PLACE_INSIDE = 3 + OPEN = 4 + CLOSE = 5 + + +def get_aabb_volume(lo, hi): + dimension = hi - lo + return dimension[0] * dimension[1] * dimension[2] + + +def detect_collision(bodyA, object_in_hand=None): + collision = False + for body_id in range(p.getNumBodies()): + if body_id == bodyA or body_id == object_in_hand: + continue + closest_points = p.getClosestPoints(bodyA, body_id, distance=0.01) + if len(closest_points) > 0: + collision = True + break + return collision + + +def detect_robot_collision(robot): + object_in_hand = robot.parts["right_hand"].object_in_hand + return ( + detect_collision(robot.parts["body"].body_id) + or detect_collision(robot.parts["left_hand"].body_id) + or detect_collision(robot.parts["right_hand"].body_id, object_in_hand) + ) + + +class BehaviorTAMPEnv(BehaviorEnv): + """ + iGibson Environment (OpenAI Gym interface). + Similar to BehaviorMPEnv, except (1) all high-level + actions are parameterized to make them deterministic + and thus conducive to sample-based TAMP and (2) there are + more such high-level actions. + """ + + def __init__( + self, + config_file, + scene_id=None, + mode="headless", + action_timestep=1 / 10.0, + physics_timestep=1 / 240.0, + device_idx=0, + render_to_tensor=False, + automatic_reset=False, + seed=0, + action_filter="mobile_manipulation", + use_motion_planning=False, + ): + """ + :param config_file: config_file path + :param scene_id: override scene_id in config file + :param mode: headless, gui, iggui + :param action_timestep: environment executes action per action_timestep second + :param physics_timestep: physics timestep for pybullet + :param device_idx: which GPU to run the simulation and rendering on + :param render_to_tensor: whether to render directly to pytorch tensors + :param automatic_reset: whether to automatic reset after an episode finishes + """ + super(BehaviorTAMPEnv, self).__init__( + config_file=config_file, + scene_id=scene_id, + mode=mode, + action_timestep=action_timestep, + physics_timestep=physics_timestep, + device_idx=device_idx, + render_to_tensor=render_to_tensor, + action_filter=action_filter, + seed=seed, + automatic_reset=automatic_reset, + ) + + self.obj_in_hand = None + self.use_motion_planning = use_motion_planning + self.robots[0].initial_z_offset = 0.7 + + def load_action_space(self): + self.task_relevant_objects = [ + item + for item in self.task.object_scope.values() + if isinstance(item, URDFObject) or isinstance(item, RoomFloor) + ] + self.num_objects = len(self.task_relevant_objects) + self.action_space = gym.spaces.Discrete(self.num_objects * NUM_ACTIONS) + + def get_body_ids(self, include_self=False): + ids = [] + for object in self.scene.get_objects(): + if isinstance(object, URDFObject): + ids.extend(object.body_ids) + + if include_self: + ids.append(self.robots[0].parts["left_hand"].get_body_id()) + ids.append(self.robots[0].parts["body"].get_body_id()) + + return ids + + def reset_and_release_hand(self): + self.robots[0].set_position_orientation(self.robots[0].get_position(), self.robots[0].get_orientation()) + for _ in range(100): + self.robots[0].parts["right_hand"].set_close_fraction(0) + self.robots[0].parts["right_hand"].trigger_fraction = 0 + p.stepSimulation() + + def step(self, action, continuous_params): + """ + :param action: an integer such that action % self.num_objects yields the object id, and + action // self.num_objects yields the correct action enum + :param continuous_params: a numpy vector of length MAX_ACTION_CONT_PARAMS. This represents + values derived from the continuous parameters of the action. + """ + + obj_list_id = int(action) % self.num_objects + action_primitive = int(action) // self.num_objects + obj = self.task_relevant_objects[obj_list_id] + + assert continuous_params.shape == (MAX_ACTION_CONT_PARAMS,) + + if not (isinstance(obj, BRBody) or isinstance(obj, BRHand) or isinstance(obj, BREye)): + if action_primitive == ActionPrimitives.NAVIGATE_TO: + if self.navigate_to_obj_pos(obj, continuous_params[0:2], use_motion_planning=self.use_motion_planning): + logging.debug("PRIMITIVE: navigate to {} success".format(obj.name)) + else: + logging.debug("PRIMITIVE: navigate to {} fail".format(obj.name)) + + elif action_primitive == ActionPrimitives.GRASP: + if self.obj_in_hand is None: + if isinstance(obj, URDFObject) and hasattr(obj, "states") and object_states.AABB in obj.states: + lo, hi = obj.states[object_states.AABB].get_value() + volume = get_aabb_volume(lo, hi) + if ( + volume < 0.2 * 0.2 * 0.2 and not obj.main_body_is_fixed + ): # say we can only grasp small objects + if ( + np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) + < 2 + ): + self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) + logging.debug( + "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) + ) + else: + logging.debug("PRIMITIVE: grasp {} fail, too far".format(obj.name)) + else: + logging.debug("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) + + # elif action_primitive == ActionPrimitives.PLACE_ONTOP: + # if self.obj_in_hand is not None and self.obj_in_hand != obj: + # logging.debug("PRIMITIVE:attempt to place {} ontop {}".format(self.obj_in_hand.name, obj.name)) + + # if isinstance(obj, URDFObject): + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # state = p.saveState() + # result = sample_kinematics( + # "onTop", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # logging.debug( + # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + # ) + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # p.removeState(state) + # logging.debug( + # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + + # else: + # logging.debug( + # "PRIMITIVE: place {} ontop {} fail, too far".format(self.obj_in_hand.name, obj.name) + # ) + # else: + # state = p.saveState() + # result = sample_kinematics( + # "onFloor", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # logging.debug( + # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + # ) + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # logging.debug( + # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # p.removeState(state) + + # elif action_primitive == ActionPrimitives.PLACE_INSIDE: + # if self.obj_in_hand is not None and self.obj_in_hand != obj and isinstance(obj, URDFObject): + # logging.debug("PRIMITIVE:attempt to place {} inside {}".format(self.obj_in_hand.name, obj.name)) + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if ( + # hasattr(obj, "states") + # and object_states.Open in obj.states + # and obj.states[object_states.Open].get_value() + # ) or (hasattr(obj, "states") and not object_states.Open in obj.states): + # state = p.saveState() + # result = sample_kinematics( + # "inside", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # logging.debug( + # "PRIMITIVE: place {} inside {} success".format(self.obj_in_hand.name, obj.name) + # ) + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # logging.debug( + # "PRIMITIVE: place {} inside {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # p.removeState(state) + # else: + # logging.debug( + # "PRIMITIVE: place {} inside {} fail, need open not open".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # else: + # logging.debug( + # "PRIMITIVE: place {} inside {} fail, too far".format(self.obj_in_hand.name, obj.name) + # ) + # elif action_primitive == ActionPrimitives.OPEN: + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if hasattr(obj, "states") and object_states.Open in obj.states: + # obj.states[object_states.Open].set_value(True) + # else: + # logging.debug("PRIMITIVE open failed, cannot be opened") + # else: + # logging.debug("PRIMITIVE open failed, too far") + + # elif action_primitive == ActionPrimitives.CLOSE: + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if hasattr(obj, "states") and object_states.Open in obj.states: + # obj.states[object_states.Open].set_value(False) + # else: + # logging.debug("PRIMITIVE close failed, cannot be opened") + # else: + # logging.debug("PRIMITIVE close failed, too far") + + state, reward, done, info = super(BehaviorTAMPEnv, self).step(np.zeros(17)) + logging.debug("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) + return state, reward, done, info + + def step_with_exec_info(self, action, continuous_params): + """ + Same as the above step method, but returns an additional param (action_exec_status) + that is True if the high-level action execution succeeded and False otherwise + """ + obj_list_id = int(action) % self.num_objects + action_primitive = int(action) // self.num_objects + action_exec_status = False + obj = self.task_relevant_objects[obj_list_id] + + assert continuous_params.shape == (MAX_ACTION_CONT_PARAMS,) + + if not (isinstance(obj, BRBody) or isinstance(obj, BRHand) or isinstance(obj, BREye)): + if action_primitive == ActionPrimitives.NAVIGATE_TO: + if self.navigate_to_obj_pos(obj, continuous_params[0:2], use_motion_planning=self.use_motion_planning): + print("PRIMITIVE: navigate to {} success".format(obj.name)) + action_exec_status = True + else: + print("PRIMITIVE: navigate to {} fail".format(obj.name)) + + elif action_primitive == ActionPrimitives.GRASP: + if self.obj_in_hand is None: + if isinstance(obj, URDFObject) and hasattr(obj, "states") and object_states.AABB in obj.states: + lo, hi = obj.states[object_states.AABB].get_value() + volume = get_aabb_volume(lo, hi) + if ( + volume < 0.2 * 0.2 * 0.2 and not obj.main_body_is_fixed + ): # say we can only grasp small objects + if ( + np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) + < 2 + ): + self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) + print( + "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) + ) + action_exec_status = True + else: + print("PRIMITIVE: grasp {} fail, too far".format(obj.name)) + else: + print("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) + + # elif action_primitive == ActionPrimitives.PLACE_ONTOP: + # if self.obj_in_hand is not None and self.obj_in_hand != obj: + # print("PRIMITIVE:attempt to place {} ontop {}".format(self.obj_in_hand.name, obj.name)) + + # if isinstance(obj, URDFObject): + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # state = p.saveState() + # result = sample_kinematics( + # "onTop", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # print( + # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + # ) + # action_exec_status = True + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # p.removeState(state) + # print( + # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + + # else: + # print( + # "PRIMITIVE: place {} ontop {} fail, too far".format(self.obj_in_hand.name, obj.name) + # ) + # else: + # state = p.saveState() + # result = sample_kinematics( + # "onFloor", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # print( + # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + # ) + # action_exec_status = True + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # print( + # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # p.removeState(state) + + # elif action_primitive == ActionPrimitives.PLACE_INSIDE: + # if self.obj_in_hand is not None and self.obj_in_hand != obj and isinstance(obj, URDFObject): + # print("PRIMITIVE:attempt to place {} inside {}".format(self.obj_in_hand.name, obj.name)) + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if ( + # hasattr(obj, "states") + # and object_states.Open in obj.states + # and obj.states[object_states.Open].get_value() + # ) or (hasattr(obj, "states") and not object_states.Open in obj.states): + # state = p.saveState() + # result = sample_kinematics( + # "inside", + # self.obj_in_hand, + # obj, + # True, + # use_ray_casting_method=True, + # max_trials=20, + # skip_falling=True, + # ) + # if result: + # print( + # "PRIMITIVE: place {} inside {} success".format(self.obj_in_hand.name, obj.name) + # ) + # action_exec_status = True + # pos = self.obj_in_hand.get_position() + # orn = self.obj_in_hand.get_orientation() + # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + # else: + # print( + # "PRIMITIVE: place {} inside {} fail, sampling fail".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # p.removeState(state) + # else: + # print( + # "PRIMITIVE: place {} inside {} fail, need open not open".format( + # self.obj_in_hand.name, obj.name + # ) + # ) + # else: + # print( + # "PRIMITIVE: place {} inside {} fail, too far".format(self.obj_in_hand.name, obj.name) + # ) + # elif action_primitive == ActionPrimitives.OPEN: + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if hasattr(obj, "states") and object_states.Open in obj.states: + # action_exec_status = True + # print("PRIMITIVE open succeeded") + # obj.states[object_states.Open].set_value(True) + # else: + # print("PRIMITIVE open failed, cannot be opened") + # else: + # print("PRIMITIVE open failed, too far") + + # elif action_primitive == ActionPrimitives.CLOSE: + # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + # if hasattr(obj, "states") and object_states.Open in obj.states: + # action_exec_status = True + # obj.states[object_states.Open].set_value(False) + # print("PRIMITIVE close succeeded") + # else: + # print("PRIMITIVE close failed, cannot be opened") + # else: + # print("PRIMITIVE close failed, too far") + + else: + print(f"Attempted to execute a High Level Action whose target was the robot's body! This is incorrect, please pass a sensible high level action argument!") + + state, reward, done, info = super(BehaviorTAMPEnv, self).step(np.zeros(17)) + print("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) + return state, reward, done, info, action_exec_status + + def grasp_obj(self, obj, use_motion_planning=False): + if use_motion_planning: + x, y, _ = obj.get_position() + z = obj.states[object_states.AABB].get_value()[1][2] + hand_x, hand_y, hand_z = self.robots[0].parts["right_hand"].get_position() + + # add a little randomness to avoid getting stuck + x += np.random.uniform(-0.025, 0.025) + y += np.random.uniform(-0.025, 0.025) + z += np.random.uniform(-0.025, 0.025) + + minx = min(x, hand_x) - 0.5 + miny = min(y, hand_y) - 0.5 + minz = min(z, hand_z) - 0.5 + maxx = max(x, hand_x) + 0.5 + maxy = max(y, hand_y) + 0.5 + maxz = max(z, hand_z) + 0.5 + + state = p.saveState() + # plan a motion to above the object + plan = plan_hand_motion_br( + robot=self.robots[0], + obj_in_hand=None, + end_conf=[x, y, z + 0.05, 0, np.pi * 5 / 6.0, np.random.uniform(-np.pi, np.pi)], + hand_limits=((minx, miny, minz), (maxx, maxy, maxz)), + obstacles=self.get_body_ids(include_self=True), + ) + p.restoreState(state) + p.removeState(state) + + if plan is not None: + grasp_success = self.execute_grasp_plan(plan, obj) + logging.debug("grasp success", grasp_success) + if grasp_success: + self.obj_in_hand = obj + else: + logging.debug("grasp failed") + self.reset_and_release_hand() + else: + logging.debug("plan is None") + self.robots[0].set_position_orientation(self.robots[0].get_position(), self.robots[0].get_orientation()) + self.reset_and_release_hand() + else: + self.obj_in_hand = obj + obj.set_position(np.array(self.robots[0].parts["right_hand"].get_position())) + self.robots[0].parts["right_hand"].set_close_fraction(1) + self.robots[0].parts["right_hand"].trigger_fraction = 1 + p.stepSimulation() + obj.set_position(np.array(self.robots[0].parts["right_hand"].get_position())) + self.robots[0].parts["right_hand"].handle_assisted_grasping( + np.zeros( + 28, + ), + override_ag_data=(obj.body_id[0], -1), + ) + + def execute_grasp_plan(self, plan, obj): + for x, y, z, roll, pitch, yaw in plan: + self.robots[0].parts["right_hand"].move([x, y, z], p.getQuaternionFromEuler([roll, pitch, yaw])) + p.stepSimulation() + + x, y, z, roll, pitch, yaw = plan[-1] + + # lower the hand until it touches the object + for i in range(25): + self.robots[0].parts["right_hand"].move([x, y, z - i * 0.005], p.getQuaternionFromEuler([roll, pitch, yaw])) + p.stepSimulation() + + # close the hand + for _ in range(50): + self.robots[0].parts["right_hand"].set_close_fraction(1) + self.robots[0].parts["right_hand"].trigger_fraction = 1 + p.stepSimulation() + + grasp_success = ( + self.robots[0] + .parts["right_hand"] + .handle_assisted_grasping( + np.zeros( + 28, + ), + override_ag_data=(obj.body_id[0], -1), + ) + ) + + # reverse the plan and get object close to torso + for x, y, z, roll, pitch, yaw in plan[::-1]: + self.robots[0].parts["right_hand"].move([x, y, z], p.getQuaternionFromEuler([roll, pitch, yaw])) + p.stepSimulation() + + return grasp_success + + def place_obj(self, original_state, target_pos, target_orn, use_motion_planning=False): + pos = self.obj_in_hand.get_position() + p.restoreState(original_state) + p.removeState(original_state) + if not use_motion_planning: + self.reset_and_release_hand() + self.robots[0].parts["right_hand"].force_release_obj() + self.obj_in_hand.set_position_orientation(target_pos, target_orn) + self.obj_in_hand = None + + else: + x, y, z = target_pos + hand_x, hand_y, hand_z = self.robots[0].parts["right_hand"].get_position() + + minx = min(x, hand_x) - 1 + miny = min(y, hand_y) - 1 + minz = min(z, hand_z) - 0.5 + maxx = max(x, hand_x) + 1 + maxy = max(y, hand_y) + 1 + maxz = max(z, hand_z) + 0.5 + + state = p.saveState() + obstacles = self.get_body_ids() + obstacles.remove(self.obj_in_hand.body_id[0]) + plan = plan_hand_motion_br( + robot=self.robots[0], + obj_in_hand=self.obj_in_hand, + end_conf=[x, y, z + 0.1, 0, np.pi * 5 / 6.0, 0], + hand_limits=((minx, miny, minz), (maxx, maxy, maxz)), + obstacles=obstacles, + ) # + p.restoreState(state) + p.removeState(state) + + if plan: + for x, y, z, roll, pitch, yaw in plan: + self.robots[0].parts["right_hand"].move([x, y, z], p.getQuaternionFromEuler([roll, pitch, yaw])) + p.stepSimulation() + released_obj = self.obj_in_hand + self.obj_in_hand = None + + # release hand + self.reset_and_release_hand() + + # force release object to avoid dealing with stateful AG release mechanism + self.robots[0].parts["right_hand"].force_release_obj() + self.robots[0].set_position_orientation(self.robots[0].get_position(), self.robots[0].get_orientation()) + # reset hand + + # reset the released object to zero velocity + p.resetBaseVelocity(released_obj.get_body_id(), linearVelocity=[0, 0, 0], angularVelocity=[0, 0, 0]) + + # let object fall + for _ in range(100): + p.stepSimulation() + + def sample_fn(self): + random_point = self.scene.get_random_point() + x, y = random_point[1][:2] + theta = np.random.uniform(*CIRCULAR_LIMITS) + return (x, y, theta) + + def navigate_to_obj_pos(self, obj, to_pos, use_motion_planning=False): + """ + navigates the robot to a certain x,y position and selects an orientation + such that the robot is facing the object. If the navigation is infeasible, + returns an indication to this effect. + + :param obj: an object to navigate toward + :param to_pos: a length-2 numpy array (x, y) containing a position to navigate to + """ + + # test agent positions around an obj + # try to place the agent near the object, and rotate it to the object + valid_position = None # ((x,y,z),(roll, pitch, yaw)) + original_position = self.robots[0].get_position() + original_orientation = self.robots[0].get_orientation() + pos = [to_pos[0], to_pos[1], self.robots[0].initial_z_offset] + + if isinstance(obj, URDFObject): + obj_pos = obj.get_position() + pos_diff = np.array(obj_pos) - np.array(pos) + yaw_angle = np.arctan2(pos_diff[1], pos_diff[0]) + orn = [0, 0, yaw_angle] + self.robots[0].set_position_orientation(pos, p.getQuaternionFromEuler(orn)) + eye_pos = self.robots[0].parts["eye"].get_position() + ray_test_res = p.rayTest(eye_pos, obj_pos) + blocked = len(ray_test_res) > 0 and ray_test_res[0][0] != obj.get_body_id() + if not detect_robot_collision(self.robots[0]) and not blocked: + valid_position = (pos, orn) + else: + orn = [0, 0, np.pi] + if not detect_robot_collision(self.robots[0]): + valid_position = (pos, orn) + + if valid_position is not None: + if use_motion_planning: + self.robots[0].set_position_orientation(original_position, original_orientation) + plan = plan_base_motion_br( + robot=self.robots[0], + end_conf=[valid_position[0][0], valid_position[0][1], valid_position[1][2]], + base_limits=(), + obstacles=self.get_body_ids(), + override_sample_fn=self.sample_fn, + ) + + if plan is not None: + if self.mode != "headless": + dry_run_base_plan(robot=self.robots[0], plan=plan) + else: + self.robots[0].set_position_orientation( + valid_position[0], p.getQuaternionFromEuler(valid_position[1]) + ) + return True + else: + self.robots[0].set_position_orientation(original_position, original_orientation) + return False + else: + self.robots[0].set_position_orientation(valid_position[0], p.getQuaternionFromEuler(valid_position[1])) + return True + else: + self.robots[0].set_position_orientation(original_position, original_orientation) + return False + + def reset(self, resample_objects=False): + obs = super(BehaviorTAMPEnv, self).reset() + self.obj_in_hand = None + + return obs + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + "-c", + default="igibson/examples/configs/behavior.yaml", + help="which config file to use [default: use yaml files in examples/configs]", + ) + parser.add_argument( + "--mode", + "-m", + choices=["headless", "gui", "iggui", "pbgui"], + default="gui", + help="which mode for simulation (default: headless)", + ) + args = parser.parse_args() + + env = BehaviorTAMPEnv( + config_file=args.config, + mode=args.mode, + action_timestep=1.0 / 300.0, + physics_timestep=1.0 / 300.0, + use_motion_planning=True, + ) + step_time_list = [] + for episode in range(100): + print("Episode: {}".format(episode)) + start = time.time() + env.reset() + for i in range(1000): # 10 seconds + action = env.action_space.sample() + state, reward, done, info = env.step(action) + print(reward, info) + if done: + break + print("Episode finished after {} timesteps, took {} seconds.".format(env.current_step, time.time() - start)) + env.close() From f21102347be7f3cef2cc39b943b1cf3166a428f4 Mon Sep 17 00:00:00 2001 From: Tom Silver Date: Wed, 13 Oct 2021 11:45:01 -0400 Subject: [PATCH 04/45] add mac installation instructions --- mac_behavior_installation.md | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 mac_behavior_installation.md diff --git a/mac_behavior_installation.md b/mac_behavior_installation.md new file mode 100644 index 000000000..142b8adc9 --- /dev/null +++ b/mac_behavior_installation.md @@ -0,0 +1,28 @@ +## Instructions for installing behavior on mac + +1. `pip install cmake` +2. `git clone git@github.com:Learning-and-Intelligent-Systems/iGibson.git --recursive` +3. `git clone git@github.com:Learning-and-Intelligent-Systems/bddl.git` +4. Fill out https://forms.gle/GXAacjpnotKkM2An7 -- ask Nishanth for the signed form if you don't have it already. After submitting the form, make sure that you open each of the links in a new tab, otherwise you will be navigated away and will have to fill out the form again. I downloaded all of them but I'm going to ignore the third link for now. The second and third downloads are very large (40GB) and will take a long time. +5. `mkdir iGibson/igibson/data` +6. Put each of the three downloads in that new data dir. +7. unzip behavior_data_bundle.zip (about 27 GB unzipped) +8. `pip install -e ./iGibson` (this will also take a long time) +9. `pip install -e ./bddl` +10. `python -m igibson.utils.assets_utils --download_assets` + +## Hack around an issue +Issue: https://github.com/StanfordVL/iGibson/issues/122 + +To see if this issue is still present, try this: +`python iGibson/igibson/examples/behavior/behavior_env_metrics.py` + +If that results in this error: `ERROR:root:Optimized renderer is not supported on Mac` then you will need a hack. + +In `iGibson/igibson/envs/env_base.py`, in the call to `MeshRendererSettings`, change `optimized=True` to `optimized=False`. + +## Check if it worked +This should now run with a GUI: +`python iGibson/igibson/examples/behavior/behavior_env_metrics.py -m pbgui` +or a different GUI: +`python iGibson/igibson/examples/behavior/behavior_env_metrics.py -m iggui` From 71da6c9cb63da1ab155dada91ac665c54514c4e4 Mon Sep 17 00:00:00 2001 From: Tom Silver Date: Sat, 16 Oct 2021 16:46:44 -0400 Subject: [PATCH 05/45] add njk sorting books yaml --- .../configs/njk_sorting_books_full_obs.yaml | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 igibson/examples/configs/njk_sorting_books_full_obs.yaml diff --git a/igibson/examples/configs/njk_sorting_books_full_obs.yaml b/igibson/examples/configs/njk_sorting_books_full_obs.yaml new file mode 100644 index 000000000..19089a3a4 --- /dev/null +++ b/igibson/examples/configs/njk_sorting_books_full_obs.yaml @@ -0,0 +1,66 @@ +# scene +scene_id: Pomaria_1_int +clutter: false +build_graph: true +load_texture: true +pybullet_load_texture: true +should_open_all_doors: true + +# domain randomization +texture_randomization_freq: null +object_randomization_freq: null + +# robot +robot: BehaviorRobot + +# task +# task: cleaning_cupboards +task: sorting_books +task_id: 0 +online_sampling: false +target_dist_min: 1.0 +target_dist_max: 10.0 +goal_format: polar +task_obs_dim: 4 + +# reward +reward_type: l2 +success_reward: 10.0 +potential_reward_weight: 1.0 +collision_reward_weight: -0.1 + +# discount factor +discount_factor: 0.99 + +# termination condition +dist_tol: 0.36 # body width +max_step: 500 +max_collisions_allowed: 500 + +# misc config +initial_pos_z_offset: 0.1 +collision_ignore_link_a_ids: [0, 1, 2] # ignore collisions with these robot links + +# sensor spec +output: [proprioception, rgb, highlight, depth, seg, ins_seg, task_obs] +# image +fisheye: false +image_width: 1280 +image_height: 1280 +vertical_fov: 120 +# depth +depth_low: 0.0 +depth_high: 10.0 + +# sensor noise +depth_noise_rate: 0.0 +scan_noise_rate: 0.0 + +# visual objects +visual_object_at_initial_target_pos: true +target_visual_object_visible_to_agent: false + +# speed limit +hand_threshold: 0.4 +hand_speed: 0.3 +body_speed: 0.3 From b322e81cf7b09e9ca9f08798b62299b6785ebb83 Mon Sep 17 00:00:00 2001 From: Tom Silver Date: Mon, 18 Oct 2021 15:07:24 -0400 Subject: [PATCH 06/45] add reshelving books yaml --- ...jk_re-shelving_library_books_full_obs.yaml | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 igibson/examples/configs/njk_re-shelving_library_books_full_obs.yaml diff --git a/igibson/examples/configs/njk_re-shelving_library_books_full_obs.yaml b/igibson/examples/configs/njk_re-shelving_library_books_full_obs.yaml new file mode 100644 index 000000000..00e824a0c --- /dev/null +++ b/igibson/examples/configs/njk_re-shelving_library_books_full_obs.yaml @@ -0,0 +1,66 @@ +# scene +scene_id: Pomaria_1_int +clutter: false +build_graph: true +load_texture: true +pybullet_load_texture: true +should_open_all_doors: true + +# domain randomization +texture_randomization_freq: null +object_randomization_freq: null + +# robot +robot: BehaviorRobot + +# task +# task: cleaning_cupboards +task: re-shelving_library_books +task_id: 0 +online_sampling: false +target_dist_min: 1.0 +target_dist_max: 10.0 +goal_format: polar +task_obs_dim: 4 + +# reward +reward_type: l2 +success_reward: 10.0 +potential_reward_weight: 1.0 +collision_reward_weight: -0.1 + +# discount factor +discount_factor: 0.99 + +# termination condition +dist_tol: 0.36 # body width +max_step: 500 +max_collisions_allowed: 500 + +# misc config +initial_pos_z_offset: 0.1 +collision_ignore_link_a_ids: [0, 1, 2] # ignore collisions with these robot links + +# sensor spec +output: [proprioception, rgb, highlight, depth, seg, ins_seg, task_obs] +# image +fisheye: false +image_width: 1280 +image_height: 1280 +vertical_fov: 120 +# depth +depth_low: 0.0 +depth_high: 10.0 + +# sensor noise +depth_noise_rate: 0.0 +scan_noise_rate: 0.0 + +# visual objects +visual_object_at_initial_target_pos: true +target_visual_object_visible_to_agent: false + +# speed limit +hand_threshold: 0.4 +hand_speed: 0.3 +body_speed: 0.3 From a1c65abdecb93527c7e24d6827f2299c03185efe Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 19 Oct 2021 19:20:49 -0400 Subject: [PATCH 07/45] includes new grasp and navigation parameterized HLA's! * also includes utility functions to save state compatible with RetroPlan! --- igibson/envs/behavior_tamp_env.py | 153 +++++++++++++++++------------- igibson/utils/checkpoint_utils.py | 43 +++++++++ 2 files changed, 131 insertions(+), 65 deletions(-) diff --git a/igibson/envs/behavior_tamp_env.py b/igibson/envs/behavior_tamp_env.py index 4e0b713dc..f45655eb2 100644 --- a/igibson/envs/behavior_tamp_env.py +++ b/igibson/envs/behavior_tamp_env.py @@ -6,6 +6,7 @@ import gym.spaces import numpy as np import pybullet as p +import scipy from igibson import object_states from igibson.envs.behavior_env import BehaviorEnv @@ -17,7 +18,7 @@ from igibson.utils.behavior_robot_planning_utils import dry_run_base_plan, plan_base_motion_br, plan_hand_motion_br NUM_ACTIONS = 6 -MAX_ACTION_CONT_PARAMS = 2 +MAX_ACTION_CONT_PARAMS = 4 class ActionPrimitives(IntEnum): @@ -133,47 +134,47 @@ def reset_and_release_hand(self): self.robots[0].parts["right_hand"].trigger_fraction = 0 p.stepSimulation() - def step(self, action, continuous_params): - """ - :param action: an integer such that action % self.num_objects yields the object id, and - action // self.num_objects yields the correct action enum - :param continuous_params: a numpy vector of length MAX_ACTION_CONT_PARAMS. This represents - values derived from the continuous parameters of the action. - """ - - obj_list_id = int(action) % self.num_objects - action_primitive = int(action) // self.num_objects - obj = self.task_relevant_objects[obj_list_id] - - assert continuous_params.shape == (MAX_ACTION_CONT_PARAMS,) - - if not (isinstance(obj, BRBody) or isinstance(obj, BRHand) or isinstance(obj, BREye)): - if action_primitive == ActionPrimitives.NAVIGATE_TO: - if self.navigate_to_obj_pos(obj, continuous_params[0:2], use_motion_planning=self.use_motion_planning): - logging.debug("PRIMITIVE: navigate to {} success".format(obj.name)) - else: - logging.debug("PRIMITIVE: navigate to {} fail".format(obj.name)) - - elif action_primitive == ActionPrimitives.GRASP: - if self.obj_in_hand is None: - if isinstance(obj, URDFObject) and hasattr(obj, "states") and object_states.AABB in obj.states: - lo, hi = obj.states[object_states.AABB].get_value() - volume = get_aabb_volume(lo, hi) - if ( - volume < 0.2 * 0.2 * 0.2 and not obj.main_body_is_fixed - ): # say we can only grasp small objects - if ( - np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) - < 2 - ): - self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) - logging.debug( - "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) - ) - else: - logging.debug("PRIMITIVE: grasp {} fail, too far".format(obj.name)) - else: - logging.debug("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) + # def step(self, action, continuous_params): + # """ + # :param action: an integer such that action % self.num_objects yields the object id, and + # action // self.num_objects yields the correct action enum + # :param continuous_params: a numpy vector of length MAX_ACTION_CONT_PARAMS. This represents + # values derived from the continuous parameters of the action. + # """ + + # obj_list_id = int(action) % self.num_objects + # action_primitive = int(action) // self.num_objects + # obj = self.task_relevant_objects[obj_list_id] + + # assert continuous_params.shape == (MAX_ACTION_CONT_PARAMS,) + + # if not (isinstance(obj, BRBody) or isinstance(obj, BRHand) or isinstance(obj, BREye)): + # if action_primitive == ActionPrimitives.NAVIGATE_TO: + # if self.navigate_to_obj_pos(obj, continuous_params[0:2], use_motion_planning=self.use_motion_planning): + # logging.debug("PRIMITIVE: navigate to {} success".format(obj.name)) + # else: + # logging.debug("PRIMITIVE: navigate to {} fail".format(obj.name)) + + # elif action_primitive == ActionPrimitives.GRASP: + # if self.obj_in_hand is None: + # if isinstance(obj, URDFObject) and hasattr(obj, "states") and object_states.AABB in obj.states: + # lo, hi = obj.states[object_states.AABB].get_value() + # volume = get_aabb_volume(lo, hi) + # if ( + # volume < 0.2 * 0.2 * 0.2 and not obj.main_body_is_fixed + # ): # say we can only grasp small objects + # if ( + # np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) + # < 2 + # ): + # self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) + # logging.debug( + # "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) + # ) + # else: + # logging.debug("PRIMITIVE: grasp {} fail, too far".format(obj.name)) + # else: + # logging.debug("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) # elif action_primitive == ActionPrimitives.PLACE_ONTOP: # if self.obj_in_hand is not None and self.obj_in_hand != obj: @@ -297,9 +298,9 @@ def step(self, action, continuous_params): # else: # logging.debug("PRIMITIVE close failed, too far") - state, reward, done, info = super(BehaviorTAMPEnv, self).step(np.zeros(17)) - logging.debug("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) - return state, reward, done, info + # state, reward, done, info = super(BehaviorTAMPEnv, self).step(np.zeros(17)) + # logging.debug("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) + # return state, reward, done, info def step_with_exec_info(self, action, continuous_params): """ @@ -319,7 +320,7 @@ def step_with_exec_info(self, action, continuous_params): print("PRIMITIVE: navigate to {} success".format(obj.name)) action_exec_status = True else: - print("PRIMITIVE: navigate to {} fail".format(obj.name)) + print(f"PRIMITIVE: navigate to {obj.name} with params {continuous_params[0:2]} fail") elif action_primitive == ActionPrimitives.GRASP: if self.obj_in_hand is None: @@ -333,7 +334,7 @@ def step_with_exec_info(self, action, continuous_params): np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2 ): - self.grasp_obj(obj, use_motion_planning=self.use_motion_planning) + self.grasp_obj_at_pos(obj, continuous_params[0:3], use_motion_planning=self.use_motion_planning) print( "PRIMITIVE: grasp {} success, obj in hand {}".format(obj.name, self.obj_in_hand) ) @@ -342,6 +343,8 @@ def step_with_exec_info(self, action, continuous_params): print("PRIMITIVE: grasp {} fail, too far".format(obj.name)) else: print("PRIMITIVE: grasp {} fail, too big or fixed".format(obj.name)) + else: + print("PRIMITIVE: grasp {} fail, agent already has an object in hand!".format(obj.name)) # elif action_primitive == ActionPrimitives.PLACE_ONTOP: # if self.obj_in_hand is not None and self.obj_in_hand != obj: @@ -479,16 +482,18 @@ def step_with_exec_info(self, action, continuous_params): print("PRIMITIVE satisfied predicates:", info["satisfied_predicates"]) return state, reward, done, info, action_exec_status - def grasp_obj(self, obj, use_motion_planning=False): + def grasp_obj_at_pos(self, obj, grasp_offset_and_z_rot, use_motion_planning=False): if use_motion_planning: - x, y, _ = obj.get_position() - z = obj.states[object_states.AABB].get_value()[1][2] + x, y, z = obj.get_position() + x += grasp_offset_and_z_rot[0] + y += grasp_offset_and_z_rot[1] + z += grasp_offset_and_z_rot[2] hand_x, hand_y, hand_z = self.robots[0].parts["right_hand"].get_position() - # add a little randomness to avoid getting stuck - x += np.random.uniform(-0.025, 0.025) - y += np.random.uniform(-0.025, 0.025) - z += np.random.uniform(-0.025, 0.025) + # # add a little randomness to avoid getting stuck + # x += np.random.uniform(-0.025, 0.025) + # y += np.random.uniform(-0.025, 0.025) + # z += np.random.uniform(-0.025, 0.025) minx = min(x, hand_x) - 0.5 miny = min(y, hand_y) - 0.5 @@ -497,12 +502,31 @@ def grasp_obj(self, obj, use_motion_planning=False): maxy = max(y, hand_y) + 0.5 maxz = max(z, hand_z) + 0.5 + # compute the angle the hand must be in such that it can grasp the object from its current offset position + # This involves aligning the z-axis (in the world frame) of the hand with the vector that goes from the hand + # to the object. We can find the rotation matrix that accomplishes this rotation by following: + # https://math.stackexchange.com/questions/180418/calculate-rotation-matrix-to-align-vector-a-to-vector-b-in-3d + hand_to_obj_vector = np.array([x - hand_x, y - hand_y, z - hand_z]) + hand_to_obj_unit_vector = hand_to_obj_vector / np.linalg.norm(hand_to_obj_vector) + unit_z_vector = np.array(0,0,1) + c = np.dot(unit_z_vector, hand_to_obj_unit_vector) + if not c == -1.0: + v = np.cross(unit_z_vector, hand_to_obj_unit_vector) + s = np.linalg.norm(v) + v_x = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0]]) + R = np.eye(3) + v_x + np.linalg.matrix_power(v_x, 2) * ((1-c)/(s ** 2)) + r = scipy.spatial.transform.Rotation.from_matrix(R) + euler_angles = r.as_euler('xyz') + euler_angles[2] += grasp_offset_and_z_rot[3] + else: + euler_angles = np.array([0.0, np.pi, 0.0]) + state = p.saveState() # plan a motion to above the object plan = plan_hand_motion_br( robot=self.robots[0], obj_in_hand=None, - end_conf=[x, y, z + 0.05, 0, np.pi * 5 / 6.0, np.random.uniform(-np.pi, np.pi)], + end_conf=[x, y, z, euler_angles[0], euler_angles[1], euler_angles[2]], hand_limits=((minx, miny, minz), (maxx, maxy, maxz)), obstacles=self.get_body_ids(include_self=True), ) @@ -633,7 +657,7 @@ def sample_fn(self): theta = np.random.uniform(*CIRCULAR_LIMITS) return (x, y, theta) - def navigate_to_obj_pos(self, obj, to_pos, use_motion_planning=False): + def navigate_to_obj_pos(self, obj, pos_offset, use_motion_planning=False): """ navigates the robot to a certain x,y position and selects an orientation such that the robot is facing the object. If the navigation is infeasible, @@ -648,25 +672,23 @@ def navigate_to_obj_pos(self, obj, to_pos, use_motion_planning=False): valid_position = None # ((x,y,z),(roll, pitch, yaw)) original_position = self.robots[0].get_position() original_orientation = self.robots[0].get_orientation() - pos = [to_pos[0], to_pos[1], self.robots[0].initial_z_offset] - if isinstance(obj, URDFObject): + if isinstance(obj, URDFObject): # must be a URDFObject so we can get its position! obj_pos = obj.get_position() - pos_diff = np.array(obj_pos) - np.array(pos) - yaw_angle = np.arctan2(pos_diff[1], pos_diff[0]) + pos = [pos_offset[0] + obj_pos[0], pos_offset[1] + obj_pos[1], self.robots[0].initial_z_offset] + yaw_angle = np.arctan2(pos_offset[1], pos_offset[0]) orn = [0, 0, yaw_angle] self.robots[0].set_position_orientation(pos, p.getQuaternionFromEuler(orn)) eye_pos = self.robots[0].parts["eye"].get_position() ray_test_res = p.rayTest(eye_pos, obj_pos) - blocked = len(ray_test_res) > 0 and ray_test_res[0][0] != obj.get_body_id() + # Test to see if the robot is obstructed by some object, but make sure that object + # is not either the robot's body or the object we want to pick up! + blocked = len(ray_test_res) > 0 and (ray_test_res[0][0] not in (self.robots[0].parts["body"].get_body_id(), obj.get_body_id())) if not detect_robot_collision(self.robots[0]) and not blocked: valid_position = (pos, orn) - else: - orn = [0, 0, np.pi] - if not detect_robot_collision(self.robots[0]): - valid_position = (pos, orn) if valid_position is not None: + print("Position commanded is not in collision!") if use_motion_planning: self.robots[0].set_position_orientation(original_position, original_orientation) plan = plan_base_motion_br( @@ -692,6 +714,7 @@ def navigate_to_obj_pos(self, obj, to_pos, use_motion_planning=False): self.robots[0].set_position_orientation(valid_position[0], p.getQuaternionFromEuler(valid_position[1])) return True else: + print("Position commanded is in collision!") self.robots[0].set_position_orientation(original_position, original_orientation) return False diff --git a/igibson/utils/checkpoint_utils.py b/igibson/utils/checkpoint_utils.py index 6652a2ca3..d9e516454 100644 --- a/igibson/utils/checkpoint_utils.py +++ b/igibson/utils/checkpoint_utils.py @@ -5,6 +5,20 @@ import pybullet as p +def save_task_relevant_state(env, root_directory, filename="behavior_dump"): + json_path = os.path.join(root_directory, f"{filename}.json") + + # Save the dump in a file. + with open(json_path, "w") as f: + json.dump(save_task_relevant_object_and_robot_states(env), f) + +def save_sim_urdf_object_state(sim, root_directory, filename="behavior_dump"): + json_path = os.path.join(root_directory, f"{filename}.json") + + # Save the dump in a file. + with open(json_path, "w") as f: + json.dump(save_all_scene_object_and_robot_states(sim), f) + def save_checkpoint(simulator, root_directory): bullet_path = os.path.join(root_directory, "%d.bullet" % simulator.frame_count) json_path = os.path.join(root_directory, "%d.json" % simulator.frame_count) @@ -54,3 +68,32 @@ def load_internal_states(simulator, dump): robot_dumps = dump["robots"] for robot, robot_dump in zip(simulator.robots, robot_dumps): robot.load_state(robot_dump) + + +def save_task_relevant_object_and_robot_states(env): + # Dump the object state. + object_dump = {} + for obj in env.task_relevant_objects: + object_dump[obj.bddl_object_scope] = {'metadata': obj.metadata, 'asset_path': obj.model_path, 'pose': tuple(obj.get_position_orientation()), 'scale': tuple(obj.scale)} + + # Dump the robot state. + robot_dump = [] + for robot in env.simulator.robots: + robot_dump.append(robot.dump_state()) + + return {"objects": object_dump, "robots": robot_dump} + +def save_all_scene_object_and_robot_states(sim): + # Dump the object state, but only for objects of type URDFObject + # that are in the sim. + object_dump = {} + for obj in sim.scene.get_objects(): + if 'URDFObject' in str(type(obj)): + object_dump[obj.bddl_object_scope] = {'metadata': obj.metadata, 'asset_path': obj.model_path, 'pose': tuple(obj.get_position_orientation()), 'scale': tuple(obj.scale)} + + # Dump the robot state. + robot_dump = [] + for robot in sim.robots: + robot_dump.append(robot.dump_state()) + + return {"objects": object_dump, "robots": robot_dump} \ No newline at end of file From 59642289723a021e83d14bcad05520353f3cdc83 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 26 Oct 2021 16:45:57 -0400 Subject: [PATCH 08/45] changes to enable navigateTo option --- igibson/envs/behavior_env.py | 12 +- igibson/envs/behavior_tamp_env.py | 211 ++++++++++-------- .../motion/motion_planners/rrt_connect.py | 2 + igibson/object_states/utils.py | 156 +++++++++++++ .../utils/behavior_robot_planning_utils.py | 3 +- igibson/utils/sampling_utils.py | 184 +++++++++++++++ 6 files changed, 474 insertions(+), 94 deletions(-) diff --git a/igibson/envs/behavior_env.py b/igibson/envs/behavior_env.py index 97d09c8e2..3016a8d66 100644 --- a/igibson/envs/behavior_env.py +++ b/igibson/envs/behavior_env.py @@ -15,6 +15,8 @@ from igibson.robots.fetch_gripper_robot import FetchGripper from igibson.utils.checkpoint_utils import load_checkpoint from igibson.utils.ig_logging import IGLogWriter +from igibson.objects.articulated_object import URDFObject +from igibson.object_states.on_floor import RoomFloor class BehaviorEnv(iGibsonEnv): @@ -33,7 +35,7 @@ def __init__( render_to_tensor=False, automatic_reset=False, seed=0, - action_filter="all", + action_filter="mobile_manipulation", instance_id=0, episode_save_dir=None, ): @@ -64,6 +66,12 @@ def __init__( self.episode_save_dir = episode_save_dir if self.episode_save_dir is not None: os.makedirs(self.episode_save_dir, exist_ok=True) + self.task_relevant_objects = [ + item + for item in self.task.object_scope.values() + if isinstance(item, URDFObject) or isinstance(item, RoomFloor) + ] + self.num_objects = len(self.task_relevant_objects) self.log_writer = None @@ -216,7 +224,7 @@ def step(self, action): new_action[:19] = action[:19] new_action[20:27] = action[19:] # The original action space for BehaviorRobot is too wide for random exploration - new_action *= 0.05 + # new_action *= 0.05 elif isinstance(self.robots[0], FetchGripper): new_action = np.zeros((11,)) if self.action_filter == "navigation": diff --git a/igibson/envs/behavior_tamp_env.py b/igibson/envs/behavior_tamp_env.py index f45655eb2..f2e05db42 100644 --- a/igibson/envs/behavior_tamp_env.py +++ b/igibson/envs/behavior_tamp_env.py @@ -10,15 +10,26 @@ from igibson import object_states from igibson.envs.behavior_env import BehaviorEnv -from igibson.external.pybullet_tools.utils import CIRCULAR_LIMITS +from igibson.external.pybullet_tools.utils import CIRCULAR_LIMITS, get_base_difference_fn from igibson.object_states.on_floor import RoomFloor -from igibson.object_states.utils import sample_kinematics +from igibson.object_states.utils import sample_kinematics, continuous_param_kinematics from igibson.objects.articulated_object import URDFObject from igibson.robots.behavior_robot import BRBody, BREye, BRHand from igibson.utils.behavior_robot_planning_utils import dry_run_base_plan, plan_base_motion_br, plan_hand_motion_br +# from igibson.external.pybullet_tools.utils import ( +# CIRCULAR_LIMITS, +# MAX_DISTANCE, +# PI, +# birrt, +# circular_difference, +# direct_path, +# get_base_difference_fn, +# get_base_distance_fn, +# pairwise_collision, +# ) NUM_ACTIONS = 6 -MAX_ACTION_CONT_PARAMS = 4 +MAX_ACTION_CONT_PARAMS = 7 class ActionPrimitives(IntEnum): @@ -346,68 +357,70 @@ def step_with_exec_info(self, action, continuous_params): else: print("PRIMITIVE: grasp {} fail, agent already has an object in hand!".format(obj.name)) - # elif action_primitive == ActionPrimitives.PLACE_ONTOP: - # if self.obj_in_hand is not None and self.obj_in_hand != obj: - # print("PRIMITIVE:attempt to place {} ontop {}".format(self.obj_in_hand.name, obj.name)) - - # if isinstance(obj, URDFObject): - # if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: - # state = p.saveState() - # result = sample_kinematics( - # "onTop", - # self.obj_in_hand, - # obj, - # True, - # use_ray_casting_method=True, - # max_trials=20, - # skip_falling=True, - # ) - # if result: - # print( - # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) - # ) - # action_exec_status = True - # pos = self.obj_in_hand.get_position() - # orn = self.obj_in_hand.get_orientation() - # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) - # else: - # p.removeState(state) - # print( - # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( - # self.obj_in_hand.name, obj.name - # ) - # ) + elif action_primitive == ActionPrimitives.PLACE_ONTOP: + if self.obj_in_hand is not None and self.obj_in_hand != obj: + print("PRIMITIVE:attempt to place {} ontop {}".format(self.obj_in_hand.name, obj.name)) + + if isinstance(obj, URDFObject): + if np.linalg.norm(np.array(obj.get_position()) - np.array(self.robots[0].get_position())) < 2: + state = p.saveState() + result = continuous_param_kinematics( + "onTop", + self.obj_in_hand, + obj, + True, + continuous_params[0:7], + use_ray_casting_method=True, + max_trials=10, + skip_falling=True, + ) + if result: + print( + "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + ) + action_exec_status = True + pos = self.obj_in_hand.get_position() + orn = self.obj_in_hand.get_orientation() + self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + else: + p.removeState(state) + print( + "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + self.obj_in_hand.name, obj.name + ) + ) - # else: - # print( - # "PRIMITIVE: place {} ontop {} fail, too far".format(self.obj_in_hand.name, obj.name) - # ) - # else: - # state = p.saveState() - # result = sample_kinematics( - # "onFloor", - # self.obj_in_hand, - # obj, - # True, - # use_ray_casting_method=True, - # max_trials=20, - # skip_falling=True, - # ) - # if result: - # print( - # "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) - # ) - # action_exec_status = True - # pos = self.obj_in_hand.get_position() - # orn = self.obj_in_hand.get_orientation() - # self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) - # else: - # print( - # "PRIMITIVE: place {} ontop {} fail, sampling fail".format( - # self.obj_in_hand.name, obj.name - # ) - # ) - # p.removeState(state) + else: + print( + "PRIMITIVE: place {} ontop {} fail, too far".format(self.obj_in_hand.name, obj.name) + ) + else: + state = p.saveState() + result = continuous_param_kinematics( + "onFloor", + self.obj_in_hand, + obj, + True, + continuous_params[0:7], + use_ray_casting_method=True, + max_trials=10, + skip_falling=True, + ) + if result: + print( + "PRIMITIVE: place {} ontop {} success".format(self.obj_in_hand.name, obj.name) + ) + action_exec_status = True + pos = self.obj_in_hand.get_position() + orn = self.obj_in_hand.get_orientation() + self.place_obj(state, pos, orn, use_motion_planning=self.use_motion_planning) + else: + print( + "PRIMITIVE: place {} ontop {} fail, sampling fail".format( + self.obj_in_hand.name, obj.name + ) + ) + p.removeState(state) # elif action_primitive == ActionPrimitives.PLACE_INSIDE: # if self.obj_in_hand is not None and self.obj_in_hand != obj and isinstance(obj, URDFObject): @@ -563,6 +576,7 @@ def execute_grasp_plan(self, plan, obj): for x, y, z, roll, pitch, yaw in plan: self.robots[0].parts["right_hand"].move([x, y, z], p.getQuaternionFromEuler([roll, pitch, yaw])) p.stepSimulation() + # s, a = get_last_action(p) x, y, z, roll, pitch, yaw = plan[-1] @@ -651,13 +665,33 @@ def place_obj(self, original_state, target_pos, target_orn, use_motion_planning= for _ in range(100): p.stepSimulation() + # def convert_nav_action_to_17dim_action(nav_action): + # """ + # :param nav_action: a 3-dim numpy array that represents the x-position, y-position and + # z-rotation of the robot in the world frame + # """ + # curr_body_pos = self.robots[0].get_position() + # curr_body_eul = p.getEulerFromQuaternion(self.robots[0].parts["right_hand"].get_orientation()) + # curr_right_pos = self.robots[0].parts["right_hand"].get_position() + # curr_right_eul = p.getEulerFromQuaternion(self.robots[0].parts["right_hand"].get_orientation()) + + # curr_body_pos_arr = np.zeros(3) + # curr_body_pos_arr[0:2] = np.array(curr_body_pos) + # new_body_pos_arr = np.zeros(3) + # curr_body_pos_arr[0:2] = np.array(nav_action[0:2]) + # new_body_pos_arr[2] = curr_body_pos_arr[2] + # right_pos_offset = curr_body_pos_arr - np.array(curr_right_pos) + # new_right_pos = new_body_pos_arr - right_pos_offset + + + def sample_fn(self): random_point = self.scene.get_random_point() x, y = random_point[1][:2] theta = np.random.uniform(*CIRCULAR_LIMITS) return (x, y, theta) - def navigate_to_obj_pos(self, obj, pos_offset, use_motion_planning=False): + def navigate_to_obj_pos(self, obj, pos_offset): """ navigates the robot to a certain x,y position and selects an orientation such that the robot is facing the object. If the navigation is infeasible, @@ -672,6 +706,7 @@ def navigate_to_obj_pos(self, obj, pos_offset, use_motion_planning=False): valid_position = None # ((x,y,z),(roll, pitch, yaw)) original_position = self.robots[0].get_position() original_orientation = self.robots[0].get_orientation() + base_diff_fn = get_base_difference_fn() if isinstance(obj, URDFObject): # must be a URDFObject so we can get its position! obj_pos = obj.get_position() @@ -688,35 +723,29 @@ def navigate_to_obj_pos(self, obj, pos_offset, use_motion_planning=False): valid_position = (pos, orn) if valid_position is not None: - print("Position commanded is not in collision!") - if use_motion_planning: - self.robots[0].set_position_orientation(original_position, original_orientation) - plan = plan_base_motion_br( - robot=self.robots[0], - end_conf=[valid_position[0][0], valid_position[0][1], valid_position[1][2]], - base_limits=(), - obstacles=self.get_body_ids(), - override_sample_fn=self.sample_fn, - ) - - if plan is not None: - if self.mode != "headless": - dry_run_base_plan(robot=self.robots[0], plan=plan) - else: - self.robots[0].set_position_orientation( - valid_position[0], p.getQuaternionFromEuler(valid_position[1]) - ) - return True - else: - self.robots[0].set_position_orientation(original_position, original_orientation) - return False - else: - self.robots[0].set_position_orientation(valid_position[0], p.getQuaternionFromEuler(valid_position[1])) - return True + self.robots[0].set_position_orientation(original_position, original_orientation) + plan = plan_base_motion_br( + robot=self.robots[0], + end_conf=[valid_position[0][0], valid_position[0][1], valid_position[1][2]], + base_limits=(), + obstacles=self.get_body_ids(), + override_sample_fn=self.sample_fn, + ) + + plan_num_steps = len(plan) + ret_actions = np.zeros((17, plan_num_steps - 1)) + plan_arr = np.array(plan) + + for i in range(1, plan_num_steps): + ret_actions[0:3, i] = base_diff_fn(plan_arr[:, i], plan_arr[:, i-1]) + + return ret_actions, True + else: print("Position commanded is in collision!") self.robots[0].set_position_orientation(original_position, original_orientation) - return False + return np.zeros(17), False + def reset(self, resample_objects=False): obs = super(BehaviorTAMPEnv, self).reset() diff --git a/igibson/external/motion/motion_planners/rrt_connect.py b/igibson/external/motion/motion_planners/rrt_connect.py index e107678df..4e0b60961 100644 --- a/igibson/external/motion/motion_planners/rrt_connect.py +++ b/igibson/external/motion/motion_planners/rrt_connect.py @@ -21,6 +21,7 @@ def rrt_connect(q1, q2, distance_fn, sample_fn, extend_fn, collision_fn, iterati tree1, tree2 = nodes1, nodes2 if swap: tree1, tree2 = nodes2, nodes1 + s = sample_fn() last1 = argmin(lambda n: distance_fn(n.config, s), tree1) @@ -62,6 +63,7 @@ def birrt(q1, q2, distance, sample, extend, collision, if collision(q1) or collision(q2): return None path = direct_path(q1, q2, extend, collision) + if path is not None: return path for attempt in irange(restarts + 1): diff --git a/igibson/object_states/utils.py b/igibson/object_states/utils.py index 7bd6056a1..871f590a6 100644 --- a/igibson/object_states/utils.py +++ b/igibson/object_states/utils.py @@ -201,3 +201,159 @@ def sample_kinematics( break return success + +def continuous_param_kinematics( + predicate, objA, objB, binary_state, continuous_params, use_ray_casting_method=False, max_trials=100, z_offset=0.05, skip_falling=False +): + if not binary_state: + raise NotImplementedError() + + sample_on_floor = predicate == "onFloor" + + if not use_ray_casting_method and not sample_on_floor and predicate not in objB.supporting_surfaces: + return False + + objA.force_wakeup() + if not sample_on_floor: + objB.force_wakeup() + + state_id = p.saveState() + for i in range(max_trials): + pos = None + if hasattr(objA, "orientations") and objA.orientations is not None: + orientation = objA.sample_orientation() + else: + orientation = [0, 0, 0, 1] + + # Orientation needs to be set for stable_z_on_aabb to work correctly + # Position needs to be set to be very far away because the object's + # original position might be blocking rays (use_ray_casting_method=True) + old_pos = np.array([200, 200, 200]) + objA.set_position_orientation(old_pos, orientation) + + # TODO HERE Add this continuous_param + + if sample_on_floor: + # TODO Warning Place on Floor does not check + #_, pos = objB.scene.get_random_point_by_room_instance(objB.room_instance) + pos = continuous_params + + if pos is not None: + pos[2] = stable_z_on_aabb(objA.get_body_id(), ([0, 0, pos[2]], [0, 0, pos[2]])) + else: + if use_ray_casting_method: + if predicate == "onTop": + params = _ON_TOP_RAY_CASTING_SAMPLING_PARAMS + elif predicate == "inside": + params = _INSIDE_RAY_CASTING_SAMPLING_PARAMS + else: + assert False, "predicate is not onTop or inside: {}".format(predicate) + + aabb = get_aabb(objA.get_body_id()) + aabb_center, aabb_extent = get_aabb_center(aabb), get_aabb_extent(aabb) + + # TODO: Get this to work with non-URDFObject objects. + # sampling_results = sampling_utils.sample_cuboid_on_object( + # objB, + # num_samples=1, + # cuboid_dimensions=aabb_extent, + # axis_probabilities=[0, 0, 1], + # refuse_downwards=True, + # **params + # ) + sampling_results = [[continuous_params[0:3], None, continuous_params[3:7]]] + + sampling_success = sampling_utils.check_sample_cuboid_on_object( + sampling_results, + objB, + num_samples=1, + cuboid_dimensions=aabb_extent, + axis_probabilities=[0, 0, 1], + refuse_downwards=True, + **params + ) + + sampled_vector = sampling_results[0][0] + sampled_quaternion = sampling_results[0][2] + + #sampling_success = sampled_vector is not None + if sampling_success: + # Find the delta from the object's CoM to its AABB centroid + diff = old_pos - aabb_center + + sample_rotation = R.from_quat(sampled_quaternion) + original_rotation = R.from_quat(orientation) + combined_rotation = sample_rotation * original_rotation + + # Rotate it using the quaternion + rotated_diff = sample_rotation.apply(diff) + + pos = sampled_vector + rotated_diff + orientation = combined_rotation.as_quat() + else: + random_idx = np.random.randint(len(objB.supporting_surfaces[predicate].keys())) + body_id, link_id = list(objB.supporting_surfaces[predicate].keys())[random_idx] + random_height_idx = np.random.randint(len(objB.supporting_surfaces[predicate][(body_id, link_id)])) + height, height_map = objB.supporting_surfaces[predicate][(body_id, link_id)][random_height_idx] + obj_half_size = np.max(objA.bounding_box) / 2 * 100 + obj_half_size_scaled = np.array([obj_half_size / objB.scale[1], obj_half_size / objB.scale[0]]) + obj_half_size_scaled = np.ceil(obj_half_size_scaled).astype(np.int) + height_map_eroded = cv2.erode(height_map, np.ones(obj_half_size_scaled, np.uint8)) + + valid_pos = np.array(height_map_eroded.nonzero()) + if valid_pos.shape[1] != 0: + random_pos_idx = np.random.randint(valid_pos.shape[1]) + random_pos = valid_pos[:, random_pos_idx] + y_map, x_map = random_pos + y = y_map / 100.0 - 2 + x = x_map / 100.0 - 2 + z = height + + pos = np.array([x, y, z]) + pos *= objB.scale + + # the supporting surface is defined w.r.t to the link frame, not + # the inertial frame + if link_id == -1: + link_pos, link_orn = p.getBasePositionAndOrientation(body_id) + dynamics_info = p.getDynamicsInfo(body_id, -1) + inertial_pos = dynamics_info[3] + inertial_orn = dynamics_info[4] + inv_inertial_pos, inv_inertial_orn = p.invertTransform(inertial_pos, inertial_orn) + link_pos, link_orn = p.multiplyTransforms( + link_pos, link_orn, inv_inertial_pos, inv_inertial_orn + ) + else: + link_pos, link_orn = get_link_pose(body_id, link_id) + pos = matrix_from_quat(link_orn).dot(pos) + np.array(link_pos) + z = stable_z_on_aabb(objA.get_body_id(), ([0, 0, pos[2]], [0, 0, pos[2]])) + pos[2] = z + + if pos is None: + success = False + else: + pos[2] += z_offset + objA.set_position_orientation(pos, orientation) + success = not detect_collision(objA.get_body_id()) # len(p.getContactPoints(objA.get_body_id())) == 0 + + if igibson.debug_sampling: + print("sample_kinematics", success) + embed() + + if success: + break + else: + p.restoreState(state_id) + + p.removeState(state_id) + + if success and not skip_falling: + objA.set_position_orientation(pos, orientation) + # Let it fall for 0.2 second + physics_timestep = p.getPhysicsEngineParameters()["fixedTimeStep"] + for _ in range(int(0.2 / physics_timestep)): + p.stepSimulation() + if len(p.getContactPoints(bodyA=objA.get_body_id())) > 0: + break + + return success diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index 3e00c8569..7a42d7e6a 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -96,13 +96,14 @@ def collision_fn(q): return None if direct: return direct_path(start_conf, end_conf, extend_fn, collision_fn) + return birrt(start_conf, end_conf, distance_fn, sample_fn, extend_fn, collision_fn, **kwargs) def dry_run_base_plan(robot: BehaviorRobot, plan): for (x, y, yaw) in plan: robot.set_position_orientation([x, y, robot.initial_z_offset], p.getQuaternionFromEuler([0, 0, yaw])) - time.sleep(0.01) + time.sleep(0.05) def dry_run_arm_plan(robot: BehaviorRobot, plan): diff --git a/igibson/utils/sampling_utils.py b/igibson/utils/sampling_utils.py index 77dcb7976..2bf15521e 100644 --- a/igibson/utils/sampling_utils.py +++ b/igibson/utils/sampling_utils.py @@ -348,6 +348,190 @@ def sample_cuboid_on_object( return results +def check_sample_cuboid_on_object( + sampling_results, + obj, + num_samples, + cuboid_dimensions, + bimodal_mean_fraction, + bimodal_stdev_fraction, + axis_probabilities, + undo_padding=False, + aabb_offset=_DEFAULT_AABB_OFFSET, + max_sampling_attempts=_DEFAULT_MAX_SAMPLING_ATTEMPTS, + max_angle_with_z_axis=_DEFAULT_MAX_ANGLE_WITH_Z_AXIS, + hit_to_plane_threshold=_DEFAULT_HIT_TO_PLANE_THRESHOLD, + refuse_downwards=False, +): + """ + Samples points on an object's surface using ray casting. + + :param obj: The object to sample points on. + :param num_samples: int, the number of points to try to sample. + :param cuboid_dimensions: Float sequence of len 3, the size of the empty cuboid we are trying to sample. Can also + provice list of cuboid dimension triplets in which case each i'th sample will be sampled using the i'th triplet. + :param bimodal_mean_fraction: float, the mean of one side of the symmetric bimodal distribution as a fraction of the + min-max range. + :param bimodal_stdev_fraction: float, the standard deviation of one side of the symmetric bimodal distribution as a + fraction of the min-max range. + :param axis_probabilities: Array of shape (3, ), the probability of ray casting along each axis. + :param undo_padding: bool. Whether the bottom padding that's applied to the cuboid should be removed before return. + Useful when the cuboid needs to be flush with the surface for whatever reason. Note that the padding will still + be applied initially (since it's not possible to do the cuboid emptiness check without doing this - otherwise + the rays will hit the sampled-on object), so the emptiness check still checks a padded cuboid. This flag will + simply make the sampler undo the padding prior to returning. + :param aabb_offset: float, padding for AABB to make sure rays start outside the actual object. + :param max_sampling_attempts: int, how many times sampling will be attempted for each requested point. + :param max_angle_with_z_axis: float, maximum angle between hit normal and positive Z axis allowed. Can be used to + disallow downward-facing hits when refuse_downwards=True. + :param hit_to_plane_threshold: float, how far any given hit position can be from the least-squares fit plane to + all of the hit positions before the sample is rejected. + :param refuse_downwards: bool, whether downward-facing hits (as defined by max_angle_with_z_axis) are allowed. + :return: List of num_samples elements where each element is a tuple in the form of + (cuboid_centroid, cuboid_up_vector, cuboid_rotation, {refusal_reason: [refusal_details...]}). Cuboid positions + are set to None when no successful sampling happens within the max number of attempts. Refusal details are only + filled if the debug_sampling flag is globally set to True. + """ + # This is imported here to avoid a circular import with object_states. + from igibson.object_states import AABB + + aabb = obj.states[AABB].get_value() + aabb_min = np.array(aabb[0]) + aabb_max = np.array(aabb[1]) + + sampling_aabb_min = aabb_min - aabb_offset + sampling_aabb_max = aabb_max + aabb_offset + + body_id = obj.get_body_id() + + cuboid_dimensions = np.array(cuboid_dimensions) + assert cuboid_dimensions.ndim <= 2 + assert cuboid_dimensions.shape[-1] == 3, "Cuboid dimensions need to contain all three dimensions." + if cuboid_dimensions.ndim == 2: + assert cuboid_dimensions.shape[0] == num_samples, "Need as many offsets as samples requested." + + results = [(None, None, None, None, defaultdict(list)) for _ in range(num_samples)] + + for i in range(num_samples): + # Sample the starting positions in advance. + samples = [[2, True, sampling_results[0][0], sampling_results[0][2]]] + + refusal_reasons = results[i][4] + + # Try each sampled position in the AABB. + for axis, is_top, start_pos, rot_quat in samples: + # Compute the ray's destination using the sampling & AABB information. + point_on_face = compute_ray_destination(axis, is_top, start_pos, aabb_min, aabb_max) + + # If we have a list of offset distances, pick the distance for this particular sample we're getting. + this_cuboid_dimensions = cuboid_dimensions if cuboid_dimensions.ndim == 1 else cuboid_dimensions[i] + + # Obtain the parallel rays using the direction sampling method. + sources, destinations, grid = get_parallel_rays(start_pos, point_on_face, this_cuboid_dimensions[:2] / 2.0) + + # Time to cast the rays. + cast_results = p.rayTestBatch(rayFromPositions=sources, rayToPositions=destinations, numThreads=0) + + # Check that all rays hit the object. + if not check_rays_hit_object(cast_results, body_id, refusal_reasons["missed_object"]): + continue + + # Process the hit positions and normals. + hit_positions = np.array([ray_res[3] for ray_res in cast_results]) + hit_normals = np.array([ray_res[4] for ray_res in cast_results]) + hit_normals /= np.linalg.norm(hit_normals, axis=1)[:, np.newaxis] + + center_idx = int(len(cast_results) / 2) + hit_link = cast_results[center_idx][1] + center_hit_normal = hit_normals[center_idx] + + # Reject anything facing more than 45deg downwards if requested. + if refuse_downwards: + if not check_hit_max_angle_from_z_axis( + center_hit_normal, max_angle_with_z_axis, refusal_reasons["downward_normal"] + ): + continue + + # Check that none of the parallel rays' hit normal differs from center ray by more than threshold. + if not check_normal_similarity(center_hit_normal, hit_normals, refusal_reasons["hit_normal_similarity"]): + continue + + # Fit a plane to the points. + plane_centroid, plane_normal = fit_plane(hit_positions) + + # The fit_plane normal can be facing either direction on the normal axis, but we want it to face away from + # the object for purposes of normal checking and padding. To do this: + # First, we get the multiplier we'd need to multiply by to get this normal to face in the positive + # direction of our axis. This is simply the sign of our vector's component in our axis. + facing_multiplier = np.sign(plane_normal[axis]) + + # Then we invert the multiplier based on whether we want it to face in the positive or negative direction + # of the axis. This is determined by whether or not is_top is True, e.g. the sampler intended this as a + # positive-facing or negative-facing sample. + facing_multiplier *= 1 if is_top else -1 + + # Finally, we multiply the entire normal vector by the multiplier to make it face the correct direction. + plane_normal *= facing_multiplier + + # Check that the plane normal is similar to the hit normal + if not check_normal_similarity( + center_hit_normal, plane_normal[None, :], refusal_reasons["plane_normal_similarity"] + ): + continue + + # Check that the points are all within some acceptable distance of the plane. + distances = get_distance_to_plane(hit_positions, plane_centroid, plane_normal) + if np.any(distances > hit_to_plane_threshold): + if igibson.debug_sampling: + refusal_reasons["dist_to_plane"].append("distances to plane: %r" % (distances,)) + continue + + # Get projection of the base onto the plane, fit a rotation, and compute the new center hit / corners. + projected_hits = get_projection_onto_plane(hit_positions, plane_centroid, plane_normal) + padding = _DEFAULT_CUBOID_BOTTOM_PADDING * plane_normal + projected_hits += padding + center_projected_hit = projected_hits[center_idx] + cuboid_centroid = center_projected_hit + plane_normal * this_cuboid_dimensions[2] / 2.0 + rotation = Rotation.from_quat(rot_quat) #compute_rotation_from_grid_sample(grid, projected_hits, cuboid_centroid, this_cuboid_dimensions) + corner_positions = cuboid_centroid[None, :] + ( + rotation.apply( + 0.5 + * this_cuboid_dimensions + * np.array( + [ + [1, 1, -1], + [-1, 1, -1], + [-1, -1, -1], + [1, -1, -1], + ] + ) + ) + ) + + # Now we use the cuboid's diagonals to check that the cuboid is actually empty. + if not check_cuboid_empty( + plane_normal, corner_positions, refusal_reasons["cuboid_not_empty"], this_cuboid_dimensions + ): + continue + + if undo_padding: + cuboid_centroid -= padding + + # We've found a nice attachment point. Continue onto next point to sample. + results[i] = (cuboid_centroid, plane_normal, rotation.as_quat(), hit_link, refusal_reasons) + break + + if igibson.debug_sampling: + print("Sampling rejection reasons:") + counter = Counter() + + for instance in results: + for reason, refusals in instance[-1].items(): + counter[reason] += len(refusals) + + print("\n".join("%s: %d" % pair for pair in counter.items())) + + return results[0][0] is not None def compute_rotation_from_grid_sample(two_d_grid, hit_positions, cuboid_centroid, this_cuboid_dimensions): # TODO: Figure out if the normalization has any advantages. From 6ad053b28baf499f51617647e8128660d697d1b2 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 4 Nov 2021 12:50:26 -0400 Subject: [PATCH 09/45] updates to enable navigateTo option to work deterministically --- igibson/envs/behavior_env.py | 6 +- igibson/envs/behavior_tamp_env.py | 58 +++++++++++++++++++ .../motion/motion_planners/rrt_connect.py | 3 +- igibson/external/pybullet_tools/utils.py | 3 +- igibson/object_states/utils.py | 2 + igibson/robots/behavior_robot.py | 9 ++- igibson/scenes/indoor_scene.py | 7 ++- .../utils/behavior_robot_planning_utils.py | 11 ++-- 8 files changed, 85 insertions(+), 14 deletions(-) diff --git a/igibson/envs/behavior_env.py b/igibson/envs/behavior_env.py index 3016a8d66..8701a3ef5 100644 --- a/igibson/envs/behavior_env.py +++ b/igibson/envs/behavior_env.py @@ -38,6 +38,7 @@ def __init__( action_filter="mobile_manipulation", instance_id=0, episode_save_dir=None, + rng=None ): """ :param config_file: config_file path @@ -60,7 +61,10 @@ def __init__( device_idx=device_idx, render_to_tensor=render_to_tensor, ) - self.rng = np.random.default_rng(seed=seed) + if rng is None: + self.rng = np.random.default_rng(seed=seed) + else: + self.rng = rng self.automatic_reset = automatic_reset self.reward_potential = None self.episode_save_dir = episode_save_dir diff --git a/igibson/envs/behavior_tamp_env.py b/igibson/envs/behavior_tamp_env.py index f2e05db42..9230488d0 100644 --- a/igibson/envs/behavior_tamp_env.py +++ b/igibson/envs/behavior_tamp_env.py @@ -665,6 +665,64 @@ def place_obj(self, original_state, target_pos, target_orn, use_motion_planning= for _ in range(100): p.stepSimulation() + def place_obj_plan(self, original_state, target_pos, target_orn, use_motion_planning=False): + pos = self.obj_in_hand.get_position() + p.restoreState(original_state) + p.removeState(original_state) + if not use_motion_planning: + self.reset_and_release_hand() + self.robots[0].parts["right_hand"].force_release_obj() + self.obj_in_hand.set_position_orientation(target_pos, target_orn) + self.obj_in_hand = None + + else: + x, y, z = target_pos + hand_x, hand_y, hand_z = self.robots[0].parts["right_hand"].get_position() + + minx = min(x, hand_x) - 1 + miny = min(y, hand_y) - 1 + minz = min(z, hand_z) - 0.5 + maxx = max(x, hand_x) + 1 + maxy = max(y, hand_y) + 1 + maxz = max(z, hand_z) + 0.5 + + state = p.saveState() + obstacles = self.get_body_ids() + obstacles.remove(self.obj_in_hand.body_id[0]) + plan = plan_hand_motion_br( + robot=self.robots[0], + obj_in_hand=self.obj_in_hand, + end_conf=[x, y, z + 0.1, 0, np.pi * 5 / 6.0, 0], + hand_limits=((minx, miny, minz), (maxx, maxy, maxz)), + obstacles=obstacles, + ) # + p.restoreState(state) + p.removeState(state) + + if plan: + for x, y, z, roll, pitch, yaw in plan: + self.robots[0].parts["right_hand"].move([x, y, z], p.getQuaternionFromEuler([roll, pitch, yaw])) + p.stepSimulation() + released_obj = self.obj_in_hand + self.obj_in_hand = None + + # release hand + self.reset_and_release_hand() + + # force release object to avoid dealing with stateful AG release mechanism + self.robots[0].parts["right_hand"].force_release_obj() + self.robots[0].set_position_orientation(self.robots[0].get_position(), self.robots[0].get_orientation()) + # reset hand + + # reset the released object to zero velocity + p.resetBaseVelocity(released_obj.get_body_id(), linearVelocity=[0, 0, 0], angularVelocity=[0, 0, 0]) + + # let object fall + for _ in range(100): + p.stepSimulation() + + return plan + # def convert_nav_action_to_17dim_action(nav_action): # """ # :param nav_action: a 3-dim numpy array that represents the x-position, y-position and diff --git a/igibson/external/motion/motion_planners/rrt_connect.py b/igibson/external/motion/motion_planners/rrt_connect.py index 4e0b60961..242f6514f 100644 --- a/igibson/external/motion/motion_planners/rrt_connect.py +++ b/igibson/external/motion/motion_planners/rrt_connect.py @@ -41,7 +41,7 @@ def rrt_connect(q1, q2, distance_fn, sample_fn, extend_fn, collision_fn, iterati path1, path2 = last1.retrace(), last2.retrace() if swap: path1, path2 = path2, path1 - #print('{} iterations, {} nodes'.format(iteration, len(nodes1) + len(nodes2))) + print('{} iterations, {} nodes'.format(iteration, len(nodes1) + len(nodes2))) return configs(path1[:-1] + path2[::-1]) return None @@ -70,7 +70,6 @@ def birrt(q1, q2, distance, sample, extend, collision, path = rrt_connect(q1, q2, distance, sample, extend, collision, iterations=iterations) if path is not None: - #print('{} attempts'.format(attempt)) if smooth is None: return path return smooth_path(path, extend, collision, iterations=smooth) diff --git a/igibson/external/pybullet_tools/utils.py b/igibson/external/pybullet_tools/utils.py index 5b5a991f7..d46588298 100644 --- a/igibson/external/pybullet_tools/utils.py +++ b/igibson/external/pybullet_tools/utils.py @@ -2580,7 +2580,8 @@ def approximate_as_cylinder(body, **kwargs): #MAX_DISTANCE = 1e-3 -MAX_DISTANCE = 0. +# MAX_DISTANCE = 0. +MAX_DISTANCE = 0.1 def set_all_collisions(body_id, enabled=1): diff --git a/igibson/object_states/utils.py b/igibson/object_states/utils.py index 871f590a6..385d89484 100644 --- a/igibson/object_states/utils.py +++ b/igibson/object_states/utils.py @@ -334,6 +334,8 @@ def continuous_param_kinematics( else: pos[2] += z_offset objA.set_position_orientation(pos, orientation) + # TODO (wmcclinton) switch ^ with: iGibson/igibson/utils/behavior_robot_planning_utils.py -> plan_hand_motion_br + # May need to get the transform of the obj relative to the hand and then use that to get the hand position and orientation after the object is placed success = not detect_collision(objA.get_body_id()) # len(p.getContactPoints(objA.get_body_id())) == 0 if igibson.debug_sampling: diff --git a/igibson/robots/behavior_robot.py b/igibson/robots/behavior_robot.py index 47f871b41..f214e4a54 100644 --- a/igibson/robots/behavior_robot.py +++ b/igibson/robots/behavior_robot.py @@ -487,14 +487,19 @@ def update(self, action): delta_pos = action[:3] delta_orn = action[3:6] clipped_delta_pos, clipped_delta_orn = self.clip_delta_pos_orn(delta_pos, delta_orn) + # Convert orientation to a quaternion clipped_delta_orn = p.getQuaternionFromEuler(clipped_delta_orn) # Calculate new body transform old_pos, old_orn = self.get_position_orientation() self.new_pos, self.new_orn = p.multiplyTransforms(old_pos, old_orn, clipped_delta_pos, clipped_delta_orn) - self.new_pos = np.round(self.new_pos, 5).tolist() - self.new_orn = np.round(self.new_orn, 5).tolist() + + # self.new_pos = np.round(self.new_pos, 5).tolist() + # self.new_orn = np.round(self.new_orn, 5).tolist() + + self.new_pos = list(self.new_pos) + self.new_orn = list(self.new_orn) # Reset agent activates the body and its collision filters reset_agent = action[19] > 0 or action[27] > 0 diff --git a/igibson/scenes/indoor_scene.py b/igibson/scenes/indoor_scene.py index d22297ecd..cb3c6898b 100644 --- a/igibson/scenes/indoor_scene.py +++ b/igibson/scenes/indoor_scene.py @@ -141,7 +141,7 @@ def build_trav_graph(self, maps_path, floor, trav_map): for node in g.nodes: trav_map[node[0], node[1]] = 255 - def get_random_point(self, floor=None): + def get_random_point(self, floor=None, rng=None): """ Sample a random point on the given floor number. If not given, sample a random floor number. @@ -153,7 +153,10 @@ def get_random_point(self, floor=None): floor = self.get_random_floor() trav = self.floor_map[floor] trav_space = np.where(trav == 255) - idx = np.random.randint(0, high=trav_space[0].shape[0]) + if rng is None: + idx = np.random.randint(0, high=trav_space[0].shape[0]) + else: + idx = rng.integers(low=0, high=trav_space[0].shape[0]) xy_map = np.array([trav_space[0][idx], trav_space[1][idx]]) x, y = self.map_to_world(xy_map) z = self.floor_heights[floor] diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index 7a42d7e6a..97489224a 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -47,7 +47,7 @@ def sample_fn(): distance_fn = get_base_distance_fn(weights=weights) body_ids = [] - for part in ["body", "left_hand", "right_hand"]: + for part in ["body", "left_hand", "right_hand", "eye"]: body_ids.append(robot.parts[part].body_id) def extend_fn(q1, q2): @@ -89,14 +89,13 @@ def collision_fn(q): yaw = p.getEulerFromQuaternion(robot.get_orientation())[2] start_conf = [pos[0], pos[1], yaw] if collision_fn(start_conf): - # print("Warning: initial configuration is in collision") + print("Warning: initial configuration is in collision") return None if collision_fn(end_conf): - # print("Warning: end configuration is in collision") + print("Warning: end configuration is in collision") return None if direct: return direct_path(start_conf, end_conf, extend_fn, collision_fn) - return birrt(start_conf, end_conf, distance_fn, sample_fn, extend_fn, collision_fn, **kwargs) @@ -197,10 +196,10 @@ def collision_fn(q): return collision if collision_fn(start_conf): - # print("Warning: initial configuration is in collision") + print("Warning: initial configuration is in collision") return None if collision_fn(end_conf): - # print("Warning: end configuration is in collision") + print("Warning: end configuration is in collision") return None if direct: return direct_path(start_conf, end_conf, extend_fn, collision_fn) From bee4e96937fa68d39765d1b47a09e17683381841 Mon Sep 17 00:00:00 2001 From: NishanthJKumar Date: Mon, 8 Nov 2021 23:53:44 -0500 Subject: [PATCH 10/45] increases RRT search iters and removes ghost hands --- igibson/activity/activity_base.py | 15 ++++++++------- igibson/external/motion/motion_planners/utils.py | 2 +- igibson/object_states/utils.py | 7 ++++--- igibson/robots/behavior_robot.py | 7 ++++--- 4 files changed, 17 insertions(+), 14 deletions(-) diff --git a/igibson/activity/activity_base.py b/igibson/activity/activity_base.py index a953ce461..124a70ef6 100644 --- a/igibson/activity/activity_base.py +++ b/igibson/activity/activity_base.py @@ -364,7 +364,7 @@ def check_scene(self): return True, feedback - def import_agent(self): + def import_agent(self, use_ghost_hands = False): cached_initial_pose = not self.online_sampling and self.scene.agent != {} if self.robot_type == BehaviorRobot: agent = BehaviorRobot(self.simulator) @@ -381,12 +381,13 @@ def import_agent(self): agent.parts["right_hand"].set_base_link_position_orientation( self.scene.agent["right_hand_1"]["xyz"], quat_from_euler(self.scene.agent["right_hand_1"]["rpy"]) ) - agent.parts["left_hand"].ghost_hand.set_base_link_position_orientation( - self.scene.agent["left_hand_1"]["xyz"], quat_from_euler(self.scene.agent["left_hand_1"]["rpy"]) - ) - agent.parts["right_hand"].ghost_hand.set_base_link_position_orientation( - self.scene.agent["right_hand_1"]["xyz"], quat_from_euler(self.scene.agent["right_hand_1"]["rpy"]) - ) + if use_ghost_hands: + agent.parts["left_hand"].ghost_hand.set_base_link_position_orientation( + self.scene.agent["left_hand_1"]["xyz"], quat_from_euler(self.scene.agent["left_hand_1"]["rpy"]) + ) + agent.parts["right_hand"].ghost_hand.set_base_link_position_orientation( + self.scene.agent["right_hand_1"]["xyz"], quat_from_euler(self.scene.agent["right_hand_1"]["rpy"]) + ) agent.parts["eye"].set_base_link_position_orientation( self.scene.agent["BREye_1"]["xyz"], quat_from_euler(self.scene.agent["BREye_1"]["rpy"]) ) diff --git a/igibson/external/motion/motion_planners/utils.py b/igibson/external/motion/motion_planners/utils.py index 4a69a07b9..95b431ec0 100644 --- a/igibson/external/motion/motion_planners/utils.py +++ b/igibson/external/motion/motion_planners/utils.py @@ -8,7 +8,7 @@ INF = float('inf') -RRT_ITERATIONS = 20 +RRT_ITERATIONS = 100 RRT_RESTARTS = 2 RRT_SMOOTHING = 20 diff --git a/igibson/object_states/utils.py b/igibson/object_states/utils.py index 385d89484..472f4cb63 100644 --- a/igibson/object_states/utils.py +++ b/igibson/object_states/utils.py @@ -347,8 +347,6 @@ def continuous_param_kinematics( else: p.restoreState(state_id) - p.removeState(state_id) - if success and not skip_falling: objA.set_position_orientation(pos, orientation) # Let it fall for 0.2 second @@ -357,5 +355,8 @@ def continuous_param_kinematics( p.stepSimulation() if len(p.getContactPoints(bodyA=objA.get_body_id())) > 0: break + + p.restoreState(state_id) + p.removeState(state_id) - return success + return success \ No newline at end of file diff --git a/igibson/robots/behavior_robot.py b/igibson/robots/behavior_robot.py index f214e4a54..3fc0bdbc4 100644 --- a/igibson/robots/behavior_robot.py +++ b/igibson/robots/behavior_robot.py @@ -114,7 +114,7 @@ def __init__( hands=("left", "right"), use_body=True, use_gripper=False, - use_ghost_hands=True, + use_ghost_hands=False, normal_color=True, show_visual_head=False, use_tracked_body_override=None, @@ -693,9 +693,10 @@ def update(self, action): # Calculate new world position based on local transform and new body pose body = self.parent.parts["body"] self.new_pos, self.new_orn = p.multiplyTransforms(body.new_pos, body.new_orn, new_local_pos, new_local_orn) + # Round to avoid numerical inaccuracies - self.new_pos = np.round(self.new_pos, 5).tolist() - self.new_orn = np.round(self.new_orn, 5).tolist() + # self.new_pos = np.round(self.new_pos, 5).tolist() + # self.new_orn = np.round(self.new_orn, 5).tolist() # Reset agent activates the body and its collision filters if self.hand == "left": From 714a624f7f3851baca8cf7b6e87a1f5e30a7e7e2 Mon Sep 17 00:00:00 2001 From: NishanthJKumar Date: Tue, 9 Nov 2021 17:52:23 -0500 Subject: [PATCH 11/45] enables passing random seed to sampling functions --- igibson/utils/behavior_robot_planning_utils.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index 97489224a..bea487e4f 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -33,11 +33,12 @@ def plan_base_motion_br( resolutions=0.05 * np.ones(3), max_distance=MAX_DISTANCE, override_sample_fn=None, + rng=np.random.default_rng(23), **kwargs ): def sample_fn(): - x, y = np.random.uniform(*base_limits) - theta = np.random.uniform(*CIRCULAR_LIMITS) + x, y = (rng.random() * (base_limits[1] - base_limits[0])) + base_limits[0] # np.random.uniform(*base_limits) + theta = (rng.random() * (CIRCULAR_LIMITS[1] - CIRCULAR_LIMITS[0])) + CIRCULAR_LIMITS[0] # np.random.uniform(*CIRCULAR_LIMITS) return (x, y, theta) if override_sample_fn is not None: @@ -142,11 +143,15 @@ def plan_hand_motion_br( weights=(1, 1, 1, 5, 5, 5), resolutions=0.02 * np.ones(6), max_distance=MAX_DISTANCE, + rng=np.random.default_rng(23), **kwargs ): def sample_fn(): - x, y, z = np.random.uniform(*hand_limits) - r, p, yaw = np.random.uniform((-PI, -PI, -PI), (PI, PI, PI)) + x, y, z = (rng.random() * (hand_limits[1] - hand_limits[0])) + hand_limits[0] #np.random.uniform(*hand_limits) + # r, p, yaw = np.random.uniform((-PI, -PI, -PI), (PI, PI, PI)) + r = (rng.random() * (2 * PI)) - PI + p = (rng.random() * (2 * PI)) - PI + yaw = (rng.random() * (2 * PI)) - PI return (x, y, z, r, p, yaw) difference_fn = get_hand_difference_fn() From fa9a776c0880384903dce5cc6ca601367f439932 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Wed, 17 Nov 2021 15:39:14 -0500 Subject: [PATCH 12/45] minor changes to enforce seeded, predictable randomness --- igibson/envs/behavior_env.py | 3 ++- igibson/utils/behavior_robot_planning_utils.py | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/igibson/envs/behavior_env.py b/igibson/envs/behavior_env.py index 8701a3ef5..3ae28ff6d 100644 --- a/igibson/envs/behavior_env.py +++ b/igibson/envs/behavior_env.py @@ -80,7 +80,8 @@ def __init__( self.log_writer = None # Make sure different parallel environments will have different random seeds - np.random.seed(os.getpid()) + # np.random.seed(os.getpid()) + np.random.seed(0) # For now, force this to be 0 so we have reproducible behavior! def load_action_space(self): """ diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index bea487e4f..431d57c9b 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -37,8 +37,8 @@ def plan_base_motion_br( **kwargs ): def sample_fn(): - x, y = (rng.random() * (base_limits[1] - base_limits[0])) + base_limits[0] # np.random.uniform(*base_limits) - theta = (rng.random() * (CIRCULAR_LIMITS[1] - CIRCULAR_LIMITS[0])) + CIRCULAR_LIMITS[0] # np.random.uniform(*CIRCULAR_LIMITS) + x, y = (rng.random() * (base_limits[1] - base_limits[0])) + base_limits[0] + theta = (rng.random() * (CIRCULAR_LIMITS[1] - CIRCULAR_LIMITS[0])) + CIRCULAR_LIMITS[0] return (x, y, theta) if override_sample_fn is not None: From cdacb118538e69ffa04ba972f5a42c900dff1314 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 21 Dec 2021 12:11:44 -0500 Subject: [PATCH 13/45] minor changes to try to get grasp option working --- igibson/external/pybullet_tools/utils.py | 3 ++- igibson/object_states/adjacency.py | 2 +- igibson/utils/behavior_robot_planning_utils.py | 9 ++++++--- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/igibson/external/pybullet_tools/utils.py b/igibson/external/pybullet_tools/utils.py index d46588298..e53b26a8d 100644 --- a/igibson/external/pybullet_tools/utils.py +++ b/igibson/external/pybullet_tools/utils.py @@ -2580,9 +2580,10 @@ def approximate_as_cylinder(body, **kwargs): #MAX_DISTANCE = 1e-3 -# MAX_DISTANCE = 0. MAX_DISTANCE = 0.1 +MAX_DISTANCE_GRASP = 1e-3 + def set_all_collisions(body_id, enabled=1): body_link_idxs = [-1] + [i for i in range(p.getNumJoints(body_id))] diff --git a/igibson/object_states/adjacency.py b/igibson/object_states/adjacency.py index 29bdde6cd..5947fd051 100644 --- a/igibson/object_states/adjacency.py +++ b/igibson/object_states/adjacency.py @@ -8,7 +8,7 @@ _MAX_ITERATIONS = 10 _MAX_DISTANCE_VERTICAL = 5.0 -_MAX_DISTANCE_HORIZONTAL = 1.0 +_MAX_DISTANCE_HORIZONTAL = 4.0 #1.0 # How many 2-D bases to try during horizontal adjacency check. When 1, only the standard axes will be considered. # When 2, standard axes + 45 degree rotated will be considered. The tried axes will be equally spaced. The higher diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index 431d57c9b..4a375a6be 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -8,6 +8,7 @@ from igibson.external.pybullet_tools.utils import ( CIRCULAR_LIMITS, MAX_DISTANCE, + MAX_DISTANCE_GRASP, PI, birrt, circular_difference, @@ -142,13 +143,15 @@ def plan_hand_motion_br( direct=False, weights=(1, 1, 1, 5, 5, 5), resolutions=0.02 * np.ones(6), - max_distance=MAX_DISTANCE, + max_distance=MAX_DISTANCE_GRASP, rng=np.random.default_rng(23), **kwargs ): def sample_fn(): - x, y, z = (rng.random() * (hand_limits[1] - hand_limits[0])) + hand_limits[0] #np.random.uniform(*hand_limits) - # r, p, yaw = np.random.uniform((-PI, -PI, -PI), (PI, PI, PI)) + # x, y, z = (rng.random() * (hand_limits[1] - hand_limits[0])) + hand_limits[0] #np.random.uniform(*hand_limits) + x = rng.random() * (hand_limits[1][0] - hand_limits[0][0]) + hand_limits[0][0] + y = rng.random() * (hand_limits[1][1] - hand_limits[0][1]) + hand_limits[0][1] + z = rng.random() * (hand_limits[1][2] - hand_limits[0][2]) + hand_limits[0][2] r = (rng.random() * (2 * PI)) - PI p = (rng.random() * (2 * PI)) - PI yaw = (rng.random() * (2 * PI)) - PI From 36f86f376c25c625eb92f423d2959accea53d94b Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 30 Dec 2021 19:37:35 -0500 Subject: [PATCH 14/45] minor deletion of comment --- igibson/robots/behavior_robot.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/igibson/robots/behavior_robot.py b/igibson/robots/behavior_robot.py index 3fc0bdbc4..931b15a66 100644 --- a/igibson/robots/behavior_robot.py +++ b/igibson/robots/behavior_robot.py @@ -1014,13 +1014,12 @@ def handle_assisted_grasping(self, action, override_ag_data=None): if override_ag_data is not None: ag_data = override_ag_data force_data = self.find_hand_contacts(find_all=True) - # print(ag_data, force_data) - # from IPython import embed; embed() if not force_data or ag_data not in force_data: return False else: ag_data = self.calculate_ag_object() + # import ipdb; ipdb.set_trace() # Return early if no AG-valid object can be grasped if not ag_data: return False From 7a388d43dc26e4b584961b831336243b60080862 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Fri, 31 Dec 2021 10:22:05 -0500 Subject: [PATCH 15/45] adds minor comment to code that gets grasped objects --- igibson/robots/behavior_robot.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/igibson/robots/behavior_robot.py b/igibson/robots/behavior_robot.py index 931b15a66..7fa816699 100644 --- a/igibson/robots/behavior_robot.py +++ b/igibson/robots/behavior_robot.py @@ -345,11 +345,15 @@ def get_proprioception(self): return state_list - def is_grasping(self, candidate_obj): + def is_grasping(self, candidate_obj_bid): + # NOTE: we need to check the candidate_obj_bid and not just the + # candidate object itself because we set parts[...].object_in_hand + # to the object's id during assistive grasping, and not the object + # itself return np.array( [ - self.parts["left_hand"].object_in_hand == candidate_obj, - self.parts["right_hand"].object_in_hand == candidate_obj, + self.parts["left_hand"].object_in_hand == candidate_obj_bid, + self.parts["right_hand"].object_in_hand == candidate_obj_bid, ] ) @@ -968,7 +972,8 @@ def calculate_ag_object(self): return None # Step 3 - Make sure we are applying a force to this object - force_data = self.find_hand_contacts() + force_data = self.find_hand_contacts() + if not force_data or (ag_bid, ag_link) not in force_data: return None @@ -1010,7 +1015,9 @@ def handle_assisted_grasping(self, action, override_ag_data=None): if not self.object_in_hand: # Detect valid trig fraction that is above threshold + # print(new_trig_frac) if new_trig_frac >= 0.0 and new_trig_frac <= 1.0 and new_trig_frac > TRIGGER_FRACTION_THRESHOLD: + # import ipdb; ipdb.set_trace() if override_ag_data is not None: ag_data = override_ag_data force_data = self.find_hand_contacts(find_all=True) @@ -1019,10 +1026,10 @@ def handle_assisted_grasping(self, action, override_ag_data=None): else: ag_data = self.calculate_ag_object() - # import ipdb; ipdb.set_trace() # Return early if no AG-valid object can be grasped if not ag_data: return False + ag_bid, ag_link = ag_data child_frame_pos, child_frame_orn = self.get_child_frame_pose(ag_bid, ag_link) @@ -1063,6 +1070,10 @@ def handle_assisted_grasping(self, action, override_ag_data=None): self.set_hand_coll_filter(ag_bid, False) self.gen_freeze_vals() return True + + else: + return False + else: constraint_violation = self.get_constraint_violation(self.obj_cid) if ( @@ -1129,7 +1140,8 @@ def update(self, action): """ # AG is only enable for the reduced joint hand if ASSIST_FRACTION > 0: - self.handle_assisted_grasping(action) + grasp_success = self.handle_assisted_grasping(action) + # import ipdb; ipdb.set_trace() super(BRHand, self).update(action) From 7a7d667bb12769cbf47c0852de90fcb0303a70f9 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sat, 1 Jan 2022 11:52:29 -0500 Subject: [PATCH 16/45] small change to get basic pick and place working with predicators! --- igibson/utils/behavior_robot_planning_utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/igibson/utils/behavior_robot_planning_utils.py b/igibson/utils/behavior_robot_planning_utils.py index 4a375a6be..d43371c1a 100644 --- a/igibson/utils/behavior_robot_planning_utils.py +++ b/igibson/utils/behavior_robot_planning_utils.py @@ -197,8 +197,12 @@ def collision_fn(q): ) if obj_in_hand is not None: + if type(obj_in_hand.body_id) == list: + obj_in_hand_body_id = obj_in_hand.body_id[0] + else: + obj_in_hand_body_id = obj_in_hand.body_id collision = collision or any( - pairwise_collision(obj_in_hand.body_id[0], obs, max_distance=max_distance) for obs in obstacles + pairwise_collision(obj_in_hand_body_id, obs, max_distance=max_distance) for obs in obstacles ) return collision From 3e6b99539e3d2891c23503c5011abd490792a92b Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 18 Jan 2022 13:28:06 -0500 Subject: [PATCH 17/45] adds changes that fix state reloading bug --- igibson/utils/checkpoint_utils.py | 10 ++++++--- igibson/utils/utils.py | 35 +++++++++++++++++++++++++++++-- 2 files changed, 40 insertions(+), 5 deletions(-) diff --git a/igibson/utils/checkpoint_utils.py b/igibson/utils/checkpoint_utils.py index d9e516454..34ed08e6f 100644 --- a/igibson/utils/checkpoint_utils.py +++ b/igibson/utils/checkpoint_utils.py @@ -4,6 +4,8 @@ import pybullet as p +from igibson.utils.utils import restoreState + def save_task_relevant_state(env, root_directory, filename="behavior_dump"): json_path = os.path.join(root_directory, f"{filename}.json") @@ -12,6 +14,7 @@ def save_task_relevant_state(env, root_directory, filename="behavior_dump"): with open(json_path, "w") as f: json.dump(save_task_relevant_object_and_robot_states(env), f) + def save_sim_urdf_object_state(sim, root_directory, filename="behavior_dump"): json_path = os.path.join(root_directory, f"{filename}.json") @@ -19,16 +22,16 @@ def save_sim_urdf_object_state(sim, root_directory, filename="behavior_dump"): with open(json_path, "w") as f: json.dump(save_all_scene_object_and_robot_states(sim), f) + def save_checkpoint(simulator, root_directory): bullet_path = os.path.join(root_directory, "%d.bullet" % simulator.frame_count) json_path = os.path.join(root_directory, "%d.json" % simulator.frame_count) - # Save the simulation state. p.saveBullet(bullet_path) - # Save the dump in a file. with open(json_path, "w") as f: json.dump(save_internal_states(simulator), f) + return simulator.frame_count def load_checkpoint(simulator, root_directory, frame): @@ -36,7 +39,8 @@ def load_checkpoint(simulator, root_directory, frame): json_path = os.path.join(root_directory, "%d.json" % frame) # Restore the simulation state. - p.restoreState(fileName=bullet_path) + # p.restoreState(fileName=bullet_path) + restoreState(fileName=bullet_path) with open(json_path, "r") as f: dump = json.load(f) diff --git a/igibson/utils/utils.py b/igibson/utils/utils.py index 393ff070d..370e79f35 100644 --- a/igibson/utils/utils.py +++ b/igibson/utils/utils.py @@ -3,8 +3,7 @@ import numpy as np -# The function to retrieve the rotation matrix changed from as_dcm to as_matrix in version 1.4 -# We will use the version number for backcompatibility +import pybullet as p import scipy import yaml from packaging import version @@ -201,3 +200,35 @@ def brighten_texture(input_filename, output_filename, brightness=1): img = np.clip(img * brightness, 0, 255) img = img.astype(np.uint8) Image.fromarray(img).save(output_filename) + + +def restoreState(*args, **kwargs): + """Restore to a given pybullet state, with a mitigation for a known sleep state restore bug. + When the pybullet state is restored, the object's wake zone (the volume around the object where + if any other object enters, the object should be waken up) does not get reset correctly, + causing weird bugs around asleep objects. This function mitigates the issue by forcing the + sleep code to update each object's wake zone. + """ + p.restoreState(*args, **kwargs) + for body_id in range(p.getNumBodies()): + p.resetBasePositionAndOrientation( + body_id, *p.getBasePositionAndOrientation(body_id), physicsClientId=kwargs.get("physicsClientId", 0) + ) + return p.restoreState(*args, **kwargs) + + +def let_user_pick(options, print_intro=True, random_selection=False): + if print_intro and not random_selection: + print("Please choose:") + for idx, element in enumerate(options): + print("{}) {}".format(idx + 1, element)) + if not random_selection: + i = input("Enter number: ") + else: + i = random.choice(range(len(options))) + 1 + try: + if 0 < int(i) <= len(options): + return int(i) + except: + pass + return None \ No newline at end of file From 9ad900f3d815ec11a6fa2900d4cd8614dd682b61 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 18 Jan 2022 15:54:38 -0500 Subject: [PATCH 18/45] Revert "Checking and maybe updating to latest changes on SVL Master" --- .github/workflows/black.yml | 2 +- .github/workflows/build-containers.yml | 34 - .github/workflows/docs.yml | 32 - .github/workflows/release.yml | 35 - .github/workflows/sync-repos.yml | 18 - .github/workflows/tests.yml | 85 - .gitignore | 5 +- .pre-commit-config.yaml | 2 +- Jenkinsfile | 89 + README.md | 26 +- docker/.env | 3 - docker/{tensorflow => base}/Dockerfile | 0 docker/{tensorflow => base}/build.sh | 0 docker/{tensorflow => base}/run.sh | 0 docker/behavior/Dockerfile | 42 +- docker/docker-compose.yml | 25 - docker/headless-gui/Dockerfile | 25 + docker/headless-gui/build.sh | 8 + docker/headless-gui/entrypoint.sh | 15 + docker/headless-gui/run.sh | 34 + docker/headless-gui/x-dummy.conf | 25 + docker/igibson/Dockerfile | 41 - docker/igibson2/Dockerfile | 34 + docker/{igibson => igibson2}/build.sh | 0 docker/{igibson => igibson2}/run.sh | 0 docker/jenkins/Dockerfile | 54 + docker/jenkins/build.sh | 1 + docker/pull-images.sh | 8 + docker/push-images.sh | 8 + docs/assets.md | 4 + docs/conf.py | 2 - docs/dataset.md | 128 +- docs/environments.md | 52 +- docs/examples.md | 21 - docs/extended_states.md | 82 - docs/images/extstates1.jpg | Bin 184390 -> 0 bytes docs/images/extstates2.jpg | Bin 208767 -> 0 bytes docs/images/extstates3.jpg | Bin 311473 -> 0 bytes docs/images/extstates4.jpg | Bin 192008 -> 0 bytes docs/images/extstates4a.jpg | Bin 96705 -> 0 bytes docs/images/extstates4b.jpg | Bin 105367 -> 0 bytes docs/images/object.png | Bin 0 -> 493450 bytes docs/images/sampling2.gif | Bin 11163272 -> 0 bytes docs/index.rst | 11 +- docs/installation.md | 139 +- docs/intro.md | 24 +- docs/objects.md | 189 +- docs/overview.md | 34 +- docs/physics_engine.md | 2 +- docs/quickstart.md | 7 +- docs/renderer.md | 85 +- docs/robots.md | 114 +- docs/sampling.md | 29 - docs/scenes.md | 173 +- docs/simulators.md | 12 +- docs/tests.md | 9 +- docs/viewer.md | 15 +- docs/virtual_reality.md | 23 +- igibson/__init__.py | 4 +- .../environments => activity}/__init__.py | 0 igibson/{tasks => activity}/bddl_backend.py | 0 .../get_obj_shelf_heights.py | 3 +- .../tests}/batch_sampling_saver.py | 0 igibson/activity/tests/sampling_loader.py | 50 + .../tests}/sampling_saver.py | 64 +- igibson/activity/tests/sampling_test.py | 33 + igibson/challenge/behavior_challenge.py | 189 ++ .../behavior_full_observability_fetch.yaml | 65 - .../behavior_onboard_sensing_fetch.yaml | 65 - igibson/configs/behavior_vr.yaml | 45 - igibson/configs/controllers/dd.yaml | 10 - igibson/configs/controllers/ik.yaml | 17 - igibson/configs/controllers/joint.yaml | 13 - igibson/configs/controllers/null_gripper.yaml | 9 - .../controllers/parallel_jaw_gripper.yaml | 13 - igibson/configs/fetch_reaching.yaml | 97 - igibson/configs/robots/ant.yaml | 14 - igibson/configs/robots/fetch.yaml | 30 - igibson/configs/robots/freight.yaml | 15 - igibson/configs/robots/husky.yaml | 15 - igibson/configs/robots/jr2.yaml | 22 - igibson/configs/robots/locobot.yaml | 15 - igibson/configs/robots/turtlebot.yaml | 15 - igibson/controllers/__init__.py | 29 - igibson/controllers/controller_base.py | 314 --- igibson/controllers/dd_controller.py | 116 -- igibson/controllers/ik_controller.py | 344 ++- igibson/controllers/joint_controller.py | 124 -- .../controllers/null_gripper_controller.py | 90 - .../parallel_jaw_gripper_controller.py | 143 -- igibson/envs/behavior_mp_env.py | 436 ++-- igibson/envs/env_base.py | 186 +- igibson/envs/igibson_env.py | 132 +- igibson/envs/igibson_ray_env.py | 2 +- igibson/envs/igibson_rllib_env.py | 4 +- igibson/envs/parallel_env.py | 2 +- igibson/examples/README.md | 19 - .../{learning => behavior}/__init__.py | 0 .../behavior/behavior_debug_replay.py | 199 ++ .../examples/behavior/behavior_debug_vr.py | 104 + .../examples/behavior/behavior_demo_batch.py | 83 + .../behavior/behavior_demo_checkpoints.py | 34 + .../behavior/behavior_demo_collection.py | 218 ++ .../behavior/behavior_demo_metrics_batch.py | 35 + .../behavior_demo_replay.py} | 195 +- .../behavior/behavior_demo_replay_rl.py | 279 +++ .../behavior/behavior_demo_segmentation.py | 471 +++++ .../behavior_demo_segmentation_batch.py | 39 + .../examples/behavior/behavior_env_metrics.py | 97 + .../behavior_generate_batch_manifest.py | 32 + .../behavior_robot_related_states_demo.py | 51 + .../configs/behavior_full_observability.yaml | 32 +- .../behavior_full_observability_fetch.yaml | 80 + .../configs/behavior_onboard_sensing.yaml | 32 +- .../behavior_onboard_sensing_fetch.yaml | 80 + .../configs/fetch_motion_planning.yaml | 33 +- .../configs/fetch_reaching.yaml} | 36 +- .../configs/fetch_room_rearrangement.yaml} | 33 +- .../configs/jr_reaching.yaml} | 24 +- .../configs/locobot_point_nav.yaml} | 23 +- .../configs/turtlebot_demo.yaml} | 22 +- .../configs/turtlebot_dynamic_nav.yaml | 19 +- .../configs/turtlebot_interactive_nav.yaml | 19 +- .../configs/turtlebot_point_nav.yaml} | 22 +- .../configs/turtlebot_point_nav_stadium.yaml} | 19 +- .../{object_states => demo}/__init__.py | 0 igibson/examples/demo/cleaning_demo.py | 43 + igibson/examples/demo/cleaning_demo_loader.py | 26 + igibson/examples/demo/cleaning_demo_saver.py | 48 + igibson/examples/demo/demo_static.py | 33 + igibson/examples/demo/env_example.py | 25 + .../examples/demo/env_interactive_example.py | 25 + .../fetch_gripper_robot_example.py | 8 +- igibson/examples/demo/igsdf_example.py | 26 + .../igsdf_obj_viz.py} | 23 +- .../igsdf_tour.py} | 13 +- .../examples/{robots => demo}/ik_example.py | 10 +- .../lidar_velodyne_example.py} | 28 +- .../mesh_renderer_example.py | 35 +- .../demo/mesh_renderer_example_pano.py | 45 + .../mesh_renderer_example_pbr.py} | 39 +- .../mesh_renderer_gpu_example.py | 7 +- .../mesh_renderer_simple_example.py | 16 +- .../motion_planning_example.py | 6 +- igibson/examples/demo/mouse_interaction.py | 91 + igibson/examples/demo/object_example.py | 43 + igibson/examples/demo/particle_demo.py | 78 + igibson/examples/demo/particle_reset_demo.py | 86 + .../examples/demo/physics_engine_example.py | 50 + .../ray_cast_sampling.py} | 35 +- igibson/examples/demo/robot_example.py | 64 + igibson/examples/demo/scene_example.py | 36 + .../demo/scene_interactive_example.py | 23 + .../scene_interactive_example_highlight.py | 32 + .../scene_interactive_object_rand_example.py | 21 + ...ene_interactive_partial_loading_example.py | 22 + .../scene_interactive_texture_rand_example.py | 18 + .../examples/demo/scene_stadium_example.py | 24 + igibson/examples/demo/simulator_example.py | 41 + igibson/examples/demo/sliceable_reset_demo.py | 84 + .../stable_baselines3_behavior_example.py | 153 ++ .../stable_baselines3_example.py | 25 +- .../temperature_example.py} | 48 +- .../examples/demo/texture_change_example.py | 42 + .../{robots => demo}/trav_map_vis_example.py | 0 .../vr_demos}/data_save_replay/vr_sr.py | 10 +- .../in_development/vr_body_tracker_test.py | 2 +- .../in_development/vr_button_mapping.py | 2 +- .../in_development/vr_cleaning_demo.py | 9 +- .../in_development/vr_hand_dex_benchmark.py | 24 +- .../in_development/vr_hand_speed_benchmark.py | 8 +- .../vr_demos}/in_development/vr_playground.py | 8 +- .../vr_demos}/in_development/vr_sample_hud.py | 12 +- .../in_development/vr_tracker_test.py | 2 +- .../{vr => demo/vr_demos}/muvr/muvr_demo.py | 10 +- .../robot_embodiment/vr_demo_robot_explore.py | 15 +- .../vr_demo_robot_grasping.py | 13 +- .../vr_demos}/test/muvr_lag_test.py | 0 .../vr_demos}/test/vr_condition_switch.py | 8 +- .../vr_demos}/test/vr_hand_geom_vis.py | 0 .../vr_demos}/test/vr_overlay_color_test.py | 12 +- .../test/vr_scroll_wrap_text_test.py | 12 +- .../{vr => demo/vr_demos}/vr_gaze_test.py | 14 +- .../{vr => demo/vr_demos}/vr_simple_demo.py | 8 +- .../web_ui/sampling_feedback_test.py | 0 .../examples/{ => demo}/web_ui/sampling_ui.py | 115 +- .../{ => demo}/web_ui/static/Beechwood_0.gif | Bin .../{ => demo}/web_ui/static/Merom_1.gif | Bin .../examples/{ => demo}/web_ui/static/Rs.gif | Bin .../{ => demo}/web_ui/static/fetch.jpg | Bin .../{ => demo}/web_ui/static/igibson_logo.png | Bin .../{ => demo}/web_ui/static/turtlebot.jpg | Bin .../{ => demo}/web_ui/templates/demo.html | 0 .../{ => demo}/web_ui/templates/finished.jpg | Bin .../{ => demo}/web_ui/templates/index.html | 0 .../{ => demo}/web_ui/templates/loading.jpg | Bin .../web_ui/templates/sampling_index.html | 0 .../{ => demo}/web_ui/templates/waiting.jpg | Bin igibson/examples/{ => demo}/web_ui/web_ui.py | 7 +- .../examples/environments/config_selector.py | 52 - .../examples/environments/env_int_example.py | 43 - .../examples/environments/env_int_selector.py | 45 - .../environments/env_nonint_example.py | 43 - .../environments/env_nonint_selector.py | 44 - igibson/examples/example_selector.py | 94 - .../scenescan2igibson => gen_data}/README.md | 0 .../combine_texture.ipynb | 0 .../combine_texture.py | 0 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}/humanoid_robot.py (79%) delete mode 100644 igibson/robots/husky.py create mode 100644 igibson/robots/husky_robot.py delete mode 100644 igibson/robots/jr2.py create mode 100644 igibson/robots/jr2_kinova_robot.py create mode 100644 igibson/robots/jr2_robot.py delete mode 100644 igibson/robots/legacy/__init__.py delete mode 100644 igibson/robots/locobot.py create mode 100644 igibson/robots/locobot_robot.py delete mode 100644 igibson/robots/locomotion_robot.py delete mode 100644 igibson/robots/manipulation_robot.py rename igibson/robots/{legacy => }/minitaur_robot.py (98%) rename igibson/robots/{legacy => }/quadrotor_robot.py (87%) create mode 100644 igibson/robots/robot_locomotor.py delete mode 100644 igibson/robots/turtlebot.py create mode 100644 igibson/robots/turtlebot_robot.py delete mode 100644 igibson/robots/two_wheel_robot.py delete mode 100644 igibson/simulator_vr.py delete mode 100644 igibson/tasks/behavior_task.py delete mode 100644 igibson/tasks/dummy_task.py delete mode 100644 igibson/termination_conditions/predicate_goal.py rename {tests => igibson/test}/benchmark/benchmark_interactive_scene.py (96%) rename {tests => igibson/test}/benchmark/benchmark_interactive_scene_rendering.py (95%) rename {tests => igibson/test}/benchmark/benchmark_pbr.py (97%) rename {tests => igibson/test}/benchmark/benchmark_static_scene.py (89%) create mode 100644 igibson/test/disabled/test_amazon_avg_obj_dims.py create mode 100644 igibson/test/disabled/test_joint_limit.py create mode 100644 igibson/test/disabled/test_physics_realism.py create mode 100644 igibson/test/disabled/test_viewer.py rename {tests => igibson/test}/test.yaml (84%) rename {tests => igibson/test}/test_binding.py (54%) create mode 100644 igibson/test/test_determinism_against_behavior.py create mode 100644 igibson/test/test_determinism_against_same_version.py create mode 100644 igibson/test/test_glfw_renderer.py rename {tests => igibson/test}/test_house.yaml (83%) rename {tests => igibson/test}/test_house_occupancy_grid.yaml (83%) rename {tests => igibson/test}/test_igibson_env.py (84%) rename {tests => igibson/test}/test_igsdf_scene_importing.py (96%) rename {tests => igibson/test}/test_motion_planning.py (86%) rename {tests => igibson/test}/test_object.py (85%) rename {tests => igibson/test}/test_pbr.py (76%) rename {tests => igibson/test}/test_render.py (77%) rename {tests => igibson/test}/test_render_tensor.py (70%) rename {tests => igibson/test}/test_robot.py (56%) rename {tests => igibson/test}/test_scene_importing.py (70%) rename {tests => igibson/test}/test_sensors.py (91%) rename {tests => igibson/test}/test_simulator.py (100%) rename {tests => igibson/test}/test_states.py (98%) create mode 100644 igibson/test/test_vr_renderer_ohoopee.py create mode 100644 igibson/test/test_vr_system.py delete mode 100644 igibson/utils/data_utils/ext_object/scripts_wip/link_bounding_box_tool.py delete mode 100644 igibson/utils/data_utils/sampling_task/sampling_loader.py delete mode 100644 igibson/utils/data_utils/sampling_task/sampling_test.py delete mode 100644 igibson/utils/python_utils.py delete mode 100644 requirements-dev.txt delete mode 100644 tests/test_determinism_against_same_version.py diff --git a/.github/workflows/black.yml b/.github/workflows/black.yml index 0bdbaaa8e..98b2a668d 100644 --- a/.github/workflows/black.yml +++ b/.github/workflows/black.yml @@ -7,4 +7,4 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - - uses: psf/black@21.12b0 \ No newline at end of file + - uses: psf/black@stable \ No newline at end of file diff --git a/.github/workflows/build-containers.yml b/.github/workflows/build-containers.yml deleted file mode 100644 index 861bee40c..000000000 --- a/.github/workflows/build-containers.yml +++ /dev/null @@ -1,34 +0,0 @@ -name: Build and Push Containers - -on: - release: - types: - - created - workflow_dispatch: - -env: - REGISTRY_USER: igibson - IMAGE_REGISTRY: docker.io - REGISTRY_PASSWORD: ${{ secrets.REGISTRY_PASSWORD }} - -jobs: - push_to_registry: - name: Push images to docker.io - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: Log in to docker.io - uses: redhat-actions/podman-login@v1 - with: - username: ${{ env.REGISTRY_USER }} - password: ${{ env.REGISTRY_PASSWORD }} - registry: ${{ env.IMAGE_REGISTRY }} - - - name: install podman-compose - run: pip3 install https://github.com/containers/podman-compose/archive/devel.tar.gz - - - name: Build and push images to quay - run: | - cd docker - podman-compose -f compose-build.yml build - podman-compose -f compose-build.yml push diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml deleted file mode 100644 index 12d4a1c8e..000000000 --- a/.github/workflows/docs.yml +++ /dev/null @@ -1,32 +0,0 @@ -name: Build & deploy docs - -on: [push] - -jobs: - build-docs: - name: Build and deploy documentation - runs-on: ubuntu-latest - if: github.repository == 'StanfordVL/iGibson' && github.ref == 'refs/heads/master' - - steps: - - name: Checkout code - uses: actions/checkout@v2 - - - name: Setup python - uses: actions/setup-python@v2 - with: - python-version: "3.8" - architecture: x64 - - - name: Install dev requirements - run: pip install -r requirements-dev.txt - - - name: Generate docs - working-directory: ./docs - run: make html - - - name: Deploy to gh-pages - uses: peaceiris/actions-gh-pages@v3 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ./docs/_build/html diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml deleted file mode 100644 index 27812afcb..000000000 --- a/.github/workflows/release.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: Release on PyPI - -on: - release: - types: - - created - -jobs: - build: - runs-on: "ubuntu-latest" - steps: - - uses: actions/checkout@master - - name: Set up Python 3.8 - uses: actions/setup-python@v1 - with: - python-version: 3.8 - - name: Install pypa/build - run: >- - python -m - pip install - build - --user - - name: Build a binary wheel and a source tarball - run: >- - python -m - build - --sdist - --outdir dist/ - . - - name: Publish a Python distribution to PyPI - if: github.repository == 'StanfordVL/iGibson' && startsWith(github.ref, 'refs/tags') - uses: pypa/gh-action-pypi-publish@release/v1 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/sync-repos.yml b/.github/workflows/sync-repos.yml deleted file mode 100644 index de70e0b3c..000000000 --- a/.github/workflows/sync-repos.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: Sync iGibson-dev/master to iGibson/master - -on: - workflow_dispatch: - schedule: - # Sync repositories on the 1st and 15th of the month at 9 AM - - cron: "0 17 1,15 * *" - -jobs: - sync-private-fork: - runs-on: ubuntu-latest - if: github.ref == 'refs/heads/master' - steps: - - uses: actions/checkout@v2 - - name: Sync iGibson-dev/master to iGibson/master - if: github.repository == 'StanfordVL/iGibson-dev' && startsWith(github.ref, 'refs/tags') - run: - git push https://$PERSONAL_ACCESS_TOKEN:x-oauth-basic@github.com/StanfordVL/iGibson.git master:master diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml deleted file mode 100644 index 41d368318..000000000 --- a/.github/workflows/tests.yml +++ /dev/null @@ -1,85 +0,0 @@ -name: Tests - -on: [pull_request] - -concurrency: - group: ${{ github.workflow }}-${{ github.event_name == 'pull_request' && github.head_ref || github.sha }} - cancel-in-progress: true - -jobs: - test: - runs-on: [self-hosted, linux, gpu] - if: github.repository == 'StanfordVL/iGibson-dev' - - steps: - - name: Checkout source - uses: actions/checkout@v2 - with: - submodules: true - path: igibson - - - name: Add CUDA to env - run: echo "/usr/local/cuda/bin" >> $GITHUB_PATH - - - name: Setup python - uses: actions/setup-python@v2 - with: - python-version: "3.8" - architecture: x64 - - - name: Install dev requirements - working-directory: igibson - run: pip install -r requirements-dev.txt - - - name: Install - working-directory: igibson - run: pip install -e . - - - name: Checkout BDDL - uses: actions/checkout@v2 - with: - repository: StanfordVL/bddl-dev - ref: 581be50e7cfd2b3a1447aaa1b4fc2424b673339c - token: ${{ secrets.PERSONAL_ACCESS_TOKEN }} # PAT is required since this is a different repo - path: bddl - submodules: recursive - lfs: true - - - name: Install BDDL - working-directory: bddl - run: pip install -e . - - - name: Link Dataset - working-directory: igibson - run: ln -s /scr/ig-data igibson/data - - # The below method of checking out ig-dataset is currently unused due to poor speed. - # - name: Create data directory - # run: mkdir -p igibson/igibson/data - # - # - name: Checkout ig_dataset - # uses: actions/checkout@v2 - # with: - # repository: StanfordVL/ig_dataset - # token: ${{ secrets.PERSONAL_ACCESS_TOKEN }} # PAT is required since this is a different repo - # path: igibson/igibson/data/ig_dataset - # submodules: recursive - # lfs: true - # - # - name: Checkout ig_assets - # uses: actions/checkout@v2 - # with: - # repository: StanfordVL/ig_assets - # token: ${{ secrets.PERSONAL_ACCESS_TOKEN }} # PAT is required since this is a different repo - # path: igibson/igibson/data/assets - # submodules: recursive - # lfs: true - - - name: Run tests - working-directory: igibson - run: pytest - - - name: Upload coverage to Codecov - uses: codecov/codecov-action@v2.1.0 - with: - token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitignore b/.gitignore index 4df65d321..72eb7d417 100644 --- a/.gitignore +++ b/.gitignore @@ -82,7 +82,4 @@ igibson/utils/data_utils/mesh_decimation/final_videos igibson/render/mesh_renderer/libcryptopp.so.8.6 # igibson run json files -*my_igibson_run.json -# Coverage -.coverage -coverage.xml +*my_igibson_run.json \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c616f2c07..9cea96e6c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/psf/black - rev: 21.12b0 + rev: stable # Replace by any tag/version: https://github.com/psf/black/tags hooks: - id: black language_version: python3 # Should be a command that runs python3.6+ diff --git a/Jenkinsfile b/Jenkinsfile new file mode 100644 index 000000000..f57094a94 --- /dev/null +++ b/Jenkinsfile @@ -0,0 +1,89 @@ +pipeline { + + agent { + docker { + image 'gibsonchallenge/gibsonv2:jenkins2' + args '--runtime=nvidia -v ${WORKSPACE}/../ig_dataset:${WORKSPACE}/igibson/data/ig_dataset' + } + } + + stages { + stage('Build') { + steps { + sh 'nvidia-smi' + sh 'pwd' + sh 'printenv' + sh 'pip install -e .' + sh 'sudo chown -R jenkins ${WORKSPACE}/igibson/data/' + } + } + + stage('Build Docs') { + steps { + sh 'sphinx-apidoc -o docs/apidoc igibson igibson/external igibson/utils/data_utils/' + sh 'sphinx-build -b html docs _sites' + } + } + + stage('Test') { + steps { + sh 'mkdir result' + sh 'pytest igibson/test/test_binding.py --junitxml=test_result/test_binding.py.xml' + sh 'pytest igibson/test/test_render.py --junitxml=test_result/test_render.py.xml' + sh 'pytest igibson/test/test_pbr.py --junitxml=test_result/test_pbr.py.xml' + sh 'pytest igibson/test/test_object.py --junitxml=test_result/test_object.py.xml' + sh 'pytest igibson/test/test_simulator.py --junitxml=test_result/test_simulator.py.xml' + sh 'pytest igibson/test/test_igibson_env.py --junitxml=test_result/test_igibson_env.py.xml' + sh 'pytest igibson/test/test_scene_importing.py --junitxml=test_result/test_scene_importing.py.xml' + sh 'pytest igibson/test/test_robot.py --junitxml=test_result/test_robot.py.xml' + sh 'pytest igibson/test/test_igsdf_scene_importing.py --junitxml=test_result/test_igsdf_scene_importing.py.xml' + sh 'pytest igibson/test/test_sensors.py --junitxml=test_result/test_sensors.py.xml' + sh 'pytest igibson/test/test_motion_planning.py --junitxml=test_result/test_motion_planning.py.xml' + sh 'pytest igibson/test/test_states.py --junitxml=test_result/test_states.py.xml' + sh 'pytest igibson/test/test_determinism_against_same_version.py --junitxml=test_result/test_determinism_against_same_version.py.xml' + } + } + + stage('Benchmark') { + steps { + sh 'python igibson/test/benchmark/benchmark_static_scene.py' + sh 'python igibson/test/benchmark/benchmark_interactive_scene.py' + } + } + + } + post { + always { + junit 'test_result/*.xml' + archiveArtifacts artifacts: 'test_result/*.xml', fingerprint: true + archiveArtifacts artifacts: '*.pdf' + archiveArtifacts artifacts: '*.png' + + publishHTML (target: [ + allowMissing: true, + alwaysLinkToLastBuild: false, + keepAll: true, + reportDir: '_sites', + reportFiles: 'index.html', + includes: '**/*', + reportName: "iGibson docs" + ]) + + cleanWs() + } + failure { + script { + // Send an email only if the build status has changed from green/unstable to red + emailext subject: '$DEFAULT_SUBJECT', + body: '$DEFAULT_CONTENT', + recipientProviders: [ + [$class: 'CulpritsRecipientProvider'], + [$class: 'DevelopersRecipientProvider'], + [$class: 'RequesterRecipientProvider'] + ], + replyTo: '$DEFAULT_REPLYTO', + to: '$DEFAULT_RECIPIENTS' + } + } + } +} diff --git a/README.md b/README.md index cf99216f7..2a0fb6eec 100644 --- a/README.md +++ b/README.md @@ -27,24 +27,24 @@ iGibson is a simulation environment providing fast visual rendering and physics If you use iGibson or its assets and models, consider citing the following publication: ``` -@misc{li2021igibson, - title={iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks}, - author={Chengshu Li and Fei Xia and Roberto Mart\'in-Mart\'in and Michael Lingelbach and Sanjana Srivastava and Bokui Shen and Kent Vainio and Cem Gokmen and Gokul Dharan and Tanish Jain and Andrey Kurenkov and Karen Liu and Hyowon Gweon and Jiajun Wu and Li Fei-Fei and Silvio Savarese}, +@misc{shen2021igibson, + title={iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes}, + author={Bokui Shen and Fei Xia and Chengshu Li and Roberto Martín-Martín and Linxi Fan and Guanzhi Wang and Claudia Pérez-D'Arpino and Shyamal Buch and Sanjana Srivastava and Lyne P. Tchapmi and Micael E. Tchapmi and Kent Vainio and Josiah Wong and Li Fei-Fei and Silvio Savarese}, year={2021}, - eprint={2108.03272}, + eprint={2012.02924}, archivePrefix={arXiv}, - primaryClass={cs.RO} + primaryClass={cs.AI} } ``` ``` -@inproceedings{shen2021igibson, - title={iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes}, - author={Bokui Shen and Fei Xia and Chengshu Li and Roberto Mart\'in-Mart\'in and Linxi Fan and Guanzhi Wang and Claudia P\'erez-D'Arpino and Shyamal Buch and Sanjana Srivastava and Lyne P. Tchapmi and Micael E. Tchapmi and Kent Vainio and Josiah Wong and Li Fei-Fei and Silvio Savarese}, - booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, +@misc{li2021igibson, + title={iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks}, + author={Chengshu Li and Fei Xia and Roberto Martín-Martín and Michael Lingelbach and Sanjana Srivastava and Bokui Shen and Kent Vainio and Cem Gokmen and Gokul Dharan and Tanish Jain and Andrey Kurenkov and Karen Liu and Hyowon Gweon and Jiajun Wu and Li Fei-Fei and Silvio Savarese}, year={2021}, - pages={accepted}, - organization={IEEE} + eprint={2108.03272}, + archivePrefix={arXiv}, + primaryClass={cs.RO} } ``` @@ -57,9 +57,9 @@ If you want to know more about iGibson, you can also check out [our webpage](htt For instructions to install iGibson and download dataset, you can visit [installation guide](http://svl.stanford.edu/igibson/docs/installation.html) and [dataset download guide](http://svl.stanford.edu/igibson/docs/dataset.html). -There are other datasets we link to iGibson. We include support to use CubiCasa5K and 3DFront scenes, adding up more than 10000 extra interactive scenes to use in iGibson! Check our [documentation](https://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils/ext_scene) on how to use those. +There are other datasets we link to iGibson. We include support to use CubiCasa5K and 3DFront scenes, adding up more than 8000 extra interactive scenes to use in iGibson! Check our documentation on how to use those. -We also maintain compatibility with datasets of 3D reconstructed large real-world scenes (homes and offices) that you can download and use with iGibson. For Gibson Dataset and Stanford 2D-3D-Semantics Dataset, please fill out this [form](https://forms.gle/36TW9uVpjrE1Mkf9A). For Matterport3D Dataset, please fill in this [form](http://dovahkiin.stanford.edu/matterport/public/MP_TOS.pdf) and send it to [matterport3d@googlegroups.com](mailto:matterport3d@googlegroups.com). Please put "use with iGibson simulator" in your email. Check our [dataset download guide](http://svl.stanford.edu/igibson/docs/dataset.html) for more details. +We also maintain compatibility with datasets of 3D reconstructed large real-world scenes (homes and offices) that you can download and use with iGibson, for example from our previous simulator, Gibson. All of them will be accessible once you fill in this [form]. ### Using iGibson with VR If you want to use iGibson VR interface, please visit the [VR guide (TBA)]. diff --git a/docker/.env b/docker/.env deleted file mode 100644 index d98efa377..000000000 --- a/docker/.env +++ /dev/null @@ -1,3 +0,0 @@ -REGISTRY=docker.io -REPO=igibson -VERSION=v2.0.4 diff --git a/docker/tensorflow/Dockerfile b/docker/base/Dockerfile similarity index 100% rename from docker/tensorflow/Dockerfile rename to docker/base/Dockerfile diff --git a/docker/tensorflow/build.sh b/docker/base/build.sh similarity index 100% rename from docker/tensorflow/build.sh rename to docker/base/build.sh diff --git a/docker/tensorflow/run.sh b/docker/base/run.sh similarity index 100% rename from docker/tensorflow/run.sh rename to docker/base/run.sh diff --git a/docker/behavior/Dockerfile b/docker/behavior/Dockerfile index 39668af25..33201887f 100644 --- a/docker/behavior/Dockerfile +++ b/docker/behavior/Dockerfile @@ -1,8 +1,38 @@ -ARG REGISTRY=docker.io -ARG REPO=igibson -ARG VERSION=v2.0.4 +FROM nvidia/cudagl:11.1.1-devel-ubuntu20.04 -FROM $REGISTRY/$REPO:$VERSION +ARG DEBIAN_FRONTEND=noninteractive -RUN git clone --depth 1 --branch master https://github.com/StanfordVL/bddl /opt/bddl --recursive -RUN pip install --no-cache-dir -e /opt/bddl +RUN apt-get update && apt-get install -y --no-install-recommends \ + cmake \ + git \ + vim \ + wget \ + curl \ + python3-dev \ + python3-opencv \ + python3-pip && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -LO http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh +RUN bash Miniconda-latest-Linux-x86_64.sh -p /miniconda -b +RUN rm Miniconda-latest-Linux-x86_64.sh +ENV PATH=/miniconda/bin:${PATH} +RUN conda update -y conda +RUN conda create -y -n igibson python=3.8.0 + +ENV PATH /miniconda/envs/igibson/bin:$PATH + +RUN pip install torch pytest ray[default,rllib] stable-baselines3 + +RUN git clone --branch ig-develop https://github.com/StanfordVL/iGibson /opt/iGibson --recursive +WORKDIR /opt/iGibson +RUN pip install -e . + +RUN git clone --branch master https://github.com/StanfordVL/BDDL /opt/BDDL --recursive +WORKDIR /opt/BDDL +RUN pip install -e . + +RUN python3 -m igibson.utils.assets_utils --download_assets +RUN python3 -m igibson.utils.assets_utils --download_demo_data + +WORKDIR /opt/iGibson/igibson diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml deleted file mode 100644 index 512092dcf..000000000 --- a/docker/docker-compose.yml +++ /dev/null @@ -1,25 +0,0 @@ -version: "3.7" - -services: - igibson: - image: ${REGISTRY}/${REPO}:${VERSION} - build: - context: ./igibson - dockerfile: Dockerfile - - igibson-tensorflow: - image: ${REGISTRY}/${REPO}:${VERSION}-tensorflow - build: - context: ./tensorflow - dockerfile: Dockerfile - - behavior: - image: ${REGISTRY}/${REPO}:${VERSION}-behavior - build: - context: ./behavior - dockerfile: Dockerfile - args: - - VERSION=${VERSION} - - REGISTRY=${REGISTRY} - - REPO=${REPO} - diff --git a/docker/headless-gui/Dockerfile b/docker/headless-gui/Dockerfile new file mode 100644 index 000000000..9e894a7d0 --- /dev/null +++ b/docker/headless-gui/Dockerfile @@ -0,0 +1,25 @@ +FROM igibson/igibson:latest + +# add dummy display and remote GUI via x11VNC + +RUN DEBIAN_FRONTEND=noninteractive apt install -y \ + xserver-xorg-video-dummy \ + xfce4 desktop-base \ + x11vnc net-tools +# disable screensaver +RUN apt autoremove -y xscreensaver + +# optional: if you want a richer desktop experience +RUN DEBIAN_FRONTEND=noninteractive apt install -y \ + xfce4-terminal firefox +RUN echo 2 | update-alternatives --config x-terminal-emulator +# ==== end of optional ===== + +RUN mkdir -p /opt/misc /opt/logs +COPY x-dummy.conf /opt/misc +COPY entrypoint.sh /opt/misc + +ENV QT_X11_NO_MITSHM=1 +ENV DISPLAY=:0 +WORKDIR /opt/igibson/igibson/examples/demo +ENTRYPOINT ["/opt/misc/entrypoint.sh"] diff --git a/docker/headless-gui/build.sh b/docker/headless-gui/build.sh new file mode 100755 index 000000000..cf79d7589 --- /dev/null +++ b/docker/headless-gui/build.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +IMAGE=igibson/igibson-gui + +export DOCKER_BUILDKIT=1 +docker build -t $IMAGE:v1.0.1 . \ + && docker tag $IMAGE:v1.0.1 $IMAGE:latest \ + && echo BUILD SUCCESSFUL diff --git a/docker/headless-gui/entrypoint.sh b/docker/headless-gui/entrypoint.sh new file mode 100755 index 000000000..a8ef16a4e --- /dev/null +++ b/docker/headless-gui/entrypoint.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash +VNC_PASSWORD=${VNC_PASSWORD:-112358} + +# start X server with dummy display on :0 +X -config /opt/misc/x-dummy.conf > /opt/logs/x-dummy.log 2>&1 & + +sleep 2 + +# start xcfe desktop +startxfce4 > /opt/logs/xcfe4.log 2>&1 & + +# start x11VNC server. Must publish port 5900 at `docker run` +x11vnc -display :0 -noxrecord -noxfixes -noxdamage -forever -passwd $VNC_PASSWORD > /opt/logs/x11vnc.log 2>&1 & + +"$@" diff --git a/docker/headless-gui/run.sh b/docker/headless-gui/run.sh new file mode 100755 index 000000000..7957845bc --- /dev/null +++ b/docker/headless-gui/run.sh @@ -0,0 +1,34 @@ +#!/usr/bin/env bash + +VNC_PORT=5900 +VNC_PASSWORD=112358 + +PARAMS= +while (( "$#" )); do + case "$1" in + -p|--vnc-port) + VNC_PORT=$2 + shift 2 + ;; + -pw|--vnc-password) + VNC_PASSWORD=$2 + shift 2 + ;; + --) # end argument parsing + shift + break + ;; + -*|--*=) # unsupported flags + echo "Error: Unsupported flag $1" >&2 + exit 1 + ;; + *) # preserve positional arguments + PARAMS="$PARAMS $1" + shift + ;; + esac +done + +echo Starting VNC server on port $VNC_PORT with password $VNC_PASSWORD +echo please run \"python simulator_example.py\" once you see the docker command prompt: +docker run --gpus all -ti -p $VNC_PORT:5900 -e VNC_PASSWORD=$VNC_PASSWORD --rm igibson/igibson-gui:latest bash \ No newline at end of file diff --git a/docker/headless-gui/x-dummy.conf b/docker/headless-gui/x-dummy.conf new file mode 100644 index 000000000..de4648bef --- /dev/null +++ b/docker/headless-gui/x-dummy.conf @@ -0,0 +1,25 @@ +Section "Monitor" + Identifier "Monitor0" + HorizSync 28.0-80.0 + VertRefresh 48.0-75.0 + # https://arachnoid.com/modelines/ + # 1920x1080 @ 60.00 Hz (GTF) hsync: 67.08 kHz; pclk: 172.80 MHz + Modeline "1920x1080_60.00" 172.80 1920 2040 2248 2576 1080 1081 1084 1118 -HSync +Vsync +EndSection + +Section "Device" + Identifier "Card0" + Driver "dummy" + VideoRam 256000 +EndSection + +Section "Screen" + DefaultDepth 24 + Identifier "Screen0" + Device "Card0" + Monitor "Monitor0" + SubSection "Display" + Depth 24 + Modes "1920x1080_60.00" + EndSubSection +EndSection \ No newline at end of file diff --git a/docker/igibson/Dockerfile b/docker/igibson/Dockerfile deleted file mode 100644 index cbf6e52b7..000000000 --- a/docker/igibson/Dockerfile +++ /dev/null @@ -1,41 +0,0 @@ -FROM nvidia/cudagl:11.3.1-base-ubuntu20.04 - -ARG DEBIAN_FRONTEND=noninteractive - -RUN apt-get update && apt-get install -y --no-install-recommends \ - cmake \ - curl \ - g++ \ - git \ - make \ - vim \ - wget \ - cuda-command-line-tools-11-3 && \ - rm -rf /var/lib/apt/lists/* - -RUN curl -LO http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -RUN bash Miniconda-latest-Linux-x86_64.sh -p /miniconda -b -RUN rm Miniconda-latest-Linux-x86_64.sh -ENV PATH=/miniconda/bin:${PATH} - -RUN conda update -y conda -RUN conda create -y -n igibson python=3.8.0 - -ENV PATH /miniconda/envs/igibson/bin:$PATH - -# NOTE: This needs to be updated in-step with the base cudagl image so the tensor renderer works -RUN pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html --no-cache-dir - -RUN git clone --depth 1 https://github.com/StanfordVL/iGibson /opt/iGibson --recursive && \ - rm -rf /opt/iGibson/igibson/render/openvr/samples - -RUN pip install --no-cache-dir -e . - -RUN pip install --no-cache-dir pytest ray[default,rllib] stable-baselines3 && rm -rf /root/.cache - -WORKDIR /opt/iGibson - -RUN python3 -m igibson.utils.assets_utils --download_assets && rm -rf /tmp/* -RUN python3 -m igibson.utils.assets_utils --download_demo_data && rm -rf /tmp/* - -WORKDIR /opt/igibson/igibson/examples/demo diff --git a/docker/igibson2/Dockerfile b/docker/igibson2/Dockerfile new file mode 100644 index 000000000..82b66c33c --- /dev/null +++ b/docker/igibson2/Dockerfile @@ -0,0 +1,34 @@ +FROM nvidia/cudagl:11.1.1-devel-ubuntu20.04 + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && apt-get install -y --no-install-recommends \ + cmake \ + git \ + wget \ + curl \ + python3-dev \ + python3-opencv \ + python3-pip && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -LO http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh +RUN bash Miniconda-latest-Linux-x86_64.sh -p /miniconda -b +RUN rm Miniconda-latest-Linux-x86_64.sh +ENV PATH=/miniconda/bin:${PATH} +RUN conda update -y conda +RUN conda create -y -n igibson python=3.8.0 + +ENV PATH /miniconda/envs/igibson/bin:$PATH + +RUN pip install --no-cache-dir https://github.com/StanfordVL/bullet3/archive/master.zip +RUN pip install torch pytest ray[default,rllib] stable-baselines3 + +RUN git clone --branch master https://github.com/StanfordVL/iGibson /opt/igibson --recursive +WORKDIR /opt/igibson +RUN pip install -e . + +RUN python3 -m igibson.utils.assets_utils --download_assets +RUN python3 -m igibson.utils.assets_utils --download_demo_data + +WORKDIR /opt/igibson/igibson/examples/demo diff --git a/docker/igibson/build.sh b/docker/igibson2/build.sh similarity index 100% rename from docker/igibson/build.sh rename to docker/igibson2/build.sh diff --git a/docker/igibson/run.sh b/docker/igibson2/run.sh similarity index 100% rename from docker/igibson/run.sh rename to docker/igibson2/run.sh diff --git a/docker/jenkins/Dockerfile b/docker/jenkins/Dockerfile new file mode 100644 index 000000000..d63f5e1de --- /dev/null +++ b/docker/jenkins/Dockerfile @@ -0,0 +1,54 @@ +from nvidia/cudagl:10.0-base-ubuntu18.04 + +ARG CUDA=10.0 +ARG CUDNN=7.6.2.24-1 + +RUN apt-get update && \ + apt-get -y install sudo + +ARG USER_ID +ARG GROUP_ID + +RUN addgroup --gid $GROUP_ID jenkins +RUN adduser --disabled-password --gecos '' --uid $USER_ID --gid $GROUP_ID jenkins + +ENV user jenkins + +RUN chown -R ${user} /home/${user} && \ + adduser ${user} sudo && \ + echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers + + +USER ${user} +WORKDIR /home/${user} + +RUN sudo apt-get install -y --no-install-recommends \ + curl build-essential git cmake \ + cuda-command-line-tools-10-0 \ + cuda-cublas-10-0 \ + cuda-cufft-10-0 \ + cuda-curand-10-0 \ + cuda-cusolver-10-0 \ + cuda-cusparse-10-0 \ + libcudnn7=${CUDNN}+cuda${CUDA} \ + vim \ + tmux \ + libhdf5-dev \ + libsm6 \ + libxext6 \ + libxrender-dev \ + wget + +# Install miniconda to /miniconda +RUN curl -LO http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh +RUN bash Miniconda-latest-Linux-x86_64.sh -p /home/${user}/miniconda -b +RUN rm Miniconda-latest-Linux-x86_64.sh +ENV PATH=/home/${user}/miniconda/bin:${PATH} +RUN conda update -y conda +RUN conda create -y -n py3-igibson python=3.6.8 +# Python packages from conda + +ENV PATH /home/${user}/miniconda/envs/py3-igibson/bin:$PATH + +RUN pip install pytest +RUN pip install Cython==0.21.1 \ No newline at end of file diff --git a/docker/jenkins/build.sh b/docker/jenkins/build.sh new file mode 100644 index 000000000..03207a8a6 --- /dev/null +++ b/docker/jenkins/build.sh @@ -0,0 +1 @@ +docker build -t gibsonchallenge/gibsonv2:jenkins2 --build-arg USER_ID=$(id -u jenkins) --build-arg GROUP_ID=$(id -g jenkins) . diff --git a/docker/pull-images.sh b/docker/pull-images.sh new file mode 100755 index 000000000..d6fc273d0 --- /dev/null +++ b/docker/pull-images.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +VERSION=1.0.1 + +docker pull igibson/igibson:v$VERSION +docker pull igibson/igibson:latest +docker pull igibson/igibson-gui:v$VERSION +docker pull igibson/igibson-gui:latest diff --git a/docker/push-images.sh b/docker/push-images.sh new file mode 100755 index 000000000..f7dfda01f --- /dev/null +++ b/docker/push-images.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +VERSION=1.0.1 + +docker push igibson/igibson:v$VERSION +docker push igibson/igibson:latest +docker push igibson/igibson-gui:v$VERSION +docker push igibson/igibson-gui:latest diff --git a/docs/assets.md b/docs/assets.md index abe3cba49..573127d3f 100644 --- a/docs/assets.md +++ b/docs/assets.md @@ -69,3 +69,7 @@ We also include [YCB objects](http://www.ycbbenchmarks.com/object-models/) in `y ## Test meshes In `test` folder we save a mesh file to test the renderer is compiled correctly, it is used by `test/test_render.py`. + +## Example configs + +Sample config files to be used with the demo. \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index ed8f39c42..7c1bbc072 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,6 @@ import sphinx_rtd_theme from recommonmark.parser import CommonMarkParser -sys.path.append(os.path.abspath("../")) import igibson project = "iGibson" @@ -51,7 +50,6 @@ "sphinx_markdown_tables", "sphinx.ext.autodoc", "sphinx.ext.mathjax", - "myst_parser", ] html_theme = "sphinx_rtd_theme" diff --git a/docs/dataset.md b/docs/dataset.md index b8a9c30df..50cc050d8 100644 --- a/docs/dataset.md +++ b/docs/dataset.md @@ -1,35 +1,39 @@ -# Datasets +Dataset +========================================== -In this page you will find information about: +In dataset we include two parts. First we introduce the new iGibson dataset in this release. Secondly, we introduce + how to download previous Gibson dataset, which is updated and compatible with iGibson. -- [How to download iGibson 2.0 scenes and the BEHAVIOR Dataset of Objects](#download-igibson-2-0-scenes-and-the-behavior-dataset-of-objects) -- [How to download iGibson 1.0 scenes](#download-igibson-1-0-scenes) -- [How to download Gibson and Stanford 2D-3D-Semantics scenes](#download-gibson-and-stanford-2d-3d-semantics-scenes) -- [How to download Matterport3D scenes](#download-matterport3d-scenes) +- [Download iGibson 2.0 Scenes and Behavior Dataset of Objects](#download-igibson-2.0-scenes-and-behavior-dataset-of-objects) +- [Download iGibson 1.0 Scenes](#download-igibson-1.0-scenes) +- [Download Gibson Scenes](#download-gibson-scenes) -## Download iGibson 2.0 Scenes and the BEHAVIOR Dataset of Objects +Download iGibson 2.0 Scenes and Behavior Dataset of Objects +------------------------- -What will you download? -- **iGibson 2.0 Dataset of Scenes**: New versions of the fully interactive scenes, more densely populated with objects. -- **BEHAVIOR Object Dataset**: Dataset of object models annotated with physical and semantic properties. The 3D models are free to use within iGibson 2.0 for BEHAVIOR (due to artists' copyright, models are encrypted and allowed only to be used with iGibson 2.0). You can download a bundle of the iGibson 2.0 dataset of scenes and the BEHAVIOR dataset of objects here. +- iGibson 2.0 Dataset of Scenes: New versions of the fully interactive scenes, more densely populated with objects. +- BEHAVIOR Object Dataset: Dataset of object models annotated with physical and semantic properties. The 3D models are free to use within iGibson 2.0 for BEHAVIOR (due to artists' copyright, models are encrypted and allowed only to be used with iGibson 2.0). You can download a bundle of the iGibson 2.0 dataset of scenes and the BEHAVIOR dataset of objects here. To download both in a bundle, you need to follow the following steps: -- Fill out the license agreement in this [form](https://docs.google.com/forms/d/e/1FAIpQLScPwhlUcHu_mwBqq5kQzT2VRIRwg_rJvF0IWYBk_LxEZiJIFg/viewform) -- You will receive a key (igibson.key). Move it into the subfolder of the iGibson repository that contains the dataset, for example, iGibson/igibson/data -- Download the behavior data bundle (ig_dataset) from [here](https://storage.googleapis.com/gibson_scenes/behavior_data_bundle.zip) -- Unzip ig_dataset into the folder: `unzip behavior_data_bundle.zip -d iGibson/igibson/data` +Request access to the BEHAVIOR assets using this [form](https://docs.google.com/forms/d/e/1FAIpQLScPwhlUcHu_mwBqq5kQzT2VRIRwg_rJvF0IWYBk_LxEZiJIFg/viewform): +- Fill out the license agreement as suggested in the [form](https://docs.google.com/forms/d/e/1FAIpQLScPwhlUcHu_mwBqq5kQzT2VRIRwg_rJvF0IWYBk_LxEZiJIFg/viewform) +- When done, copy the key you receive (igibson.key) into the repository subfolder folder iGibson/igibson/data +- Download the behavior data bundle (ig_dataset) [here](https://storage.googleapis.com/gibson_scenes/behavior_data_bundle.zip). +- Unzip ig_dataset: + - `mkdir iGibson/igibson/data` + - `unzip behavior_data_bundle.zip -d iGibson/igibson/data` -After this process, you will be able to sample and use the scenes and objects in iGibson, for example, to evaluate your embodied AI solutions in the [BEHAVIOR benchmark](https://behavior.stanford.edu/). -## Download iGibson 1.0 Scenes +Download iGibson 1.0 Scenes +------------------------ -What will you download? -- **iGibson 1.0 Dataset of Scenes**: We annotated fifteen 3D reconstructions of real-world scans and converted them into fully interactive scene models. In this process, we respect the original object-instance layout and object-category distribution. The object models are extended from open-source datasets ([ShapeNet Dataset](https://www.shapenet.org/), [Motion Dataset](http://motiondataset.zbuaa.com/), [SAPIEN Dataset](https://sapien.ucsd.edu/)) enriched with annotations of material and dynamic properties. +We annotate fifteen 3D reconstructions of real-world scans and convert them into fully interactive scene models. In this process, we respect the original object-instance layout and object-category distribution. The object models are extended from open-source datasets ([ShapeNet Dataset](https://www.shapenet.org/), [Motion Dataset](http://motiondataset.zbuaa.com/), [SAPIEN Dataset](https://sapien.ucsd.edu/)) enriched with annotations of material and dynamic properties. -The following image shows the fifteen fully interactive scenes: +The fifteen fully interactive models are visualized below. ![placeholder.jpg](images/ig_scene.png) +#### Download Instruction To download the dataset, you need to first configure where the dataset is to be stored. You can change it in `your_installation_path/igibson/global_config.yaml` (default and recommended: `ig_dataset: your_installation_path/igibson/data/ig_dataset`). iGibson scenes can be downloaded with one single line: ```bash @@ -37,38 +41,54 @@ python -m igibson.utils.assets_utils --download_ig_dataset ``` If the script fails to work, you can download from this [direct link](https://storage.googleapis.com/gibson_scenes/ig_dataset.tar.gz) and extract to `your_installation_path/igibson/data/ig_dataset`. +#### Dataset Format +The new dataset format can be found [here](https://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils). -A description of the file structure and format of the files in the dataset can be found [here](https://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils). +#### Cubicasa / 3D Front Dataset +We provide support for Cubicasa and 3D Front Dataset, to import them into iGibson, follow the guide [here](https://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils/ext_scene). -**Cubicasa / 3D Front Dataset Support:** We provide support for Cubicasa and 3D Front Dataset providing more than 10000 additional scenes (with less furniture than our fifteen scenes). To import them into iGibson, follow the instructions [here](https://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils/ext_scene). +Download Gibson Scenes +------------------------ +Original Gibson Environment Dataset has been updated to use with iGibson simulator. The link will first take you to + the license agreement and then to the data. -## Download Gibson and Stanford 2D-3D-Semantics scenes +[[ Get download link for Gibson Data ]]. -What will you download? -- **Gibson static scenes**: more than 500 reconstructions of homes and offices with a Matterport device. These models keep the texture observed with the sensor, but contain some irregularities, specially with reflective surfaces and thin elements like chairs' legs. -- **Stanford 2D-3D-Semantics scenes**: 7 reconstructions of Stanford offices. +License Note: The dataset license is included in the above link. The license in this repository covers only the provided software. -Files included in the dataset: +Files included in this distribution: -- All scenes, 572 scenes (108GB): gibson_v2_all.tar.gz -- 4+ partition, 106 scenes, with textures better packed (2.6GB): gibson_v2_4+.tar.gz -- Stanford 2D-3D-Semantics, 7 scenes (1.4GB): 2d3ds_for_igibson.zip +1. All scenes, 572 scenes (108GB): gibson_v2_all.tar.gz +2. 4+ partition, 106 scenes, with textures better packed (2.6GB): gibson_v2_4+.tar.gz +3. Demo scene `Rs` -We have updated these datasets to be used with iGibson so that users can keep developing and studying pure navigation solutions. The following link will bring you to a license agreement and then to a downloading URL: [form](https://forms.gle/36TW9uVpjrE1Mkf9A) - -After filling in the agreement, you will obtain a downloading `URL`. -You can download the data manually and store it in the path set in `your_installation_path/igibson/global_config.yaml` (default and recommended: `g_dataset: your_installation_path/igibson/data/g_dataset`). -Alternatively, you can run a single command to download the dataset, decompress, and place it in the correct folder: +To download 1 and 2, you need to fill in the agreement and get the download link `URL`, after which you can + manually download and store them in the path set in `your_installation_path/igibson/global_config.yaml` (default and + recommended: `dataset: your_installation_path/igibson/data/g_dataset`). You can run a single command to download the dataset + , this script automatically download, decompress, and put the dataset to correct place. ```bash python -m igibson.utils.assets_utils --download_dataset URL ``` -The Gibson Environment Dataset consists of 572 models and 1440 floors. We cover a diverse set of models including households, offices, hotels, venues, museums, hospitals, construction sites, etc. A diverse set of visualization of all spaces in Gibson can be seen [here](http://gibsonenv.stanford.edu/database/). -The following image shows some of the environments: +To download 3, you can run: + +```bash +python -m igibson.utils.assets_utils --download_demo_data +``` + + +### Original Gibson Environment Dataset Description (Non-interactive) + + +Full Gibson Environment Dataset consists of 572 models and 1440 floors. We cover a diverse set of models including households, offices, hotels, venues, museums, hospitals, construction sites, etc. A diverse set of visualization of all spaces in Gibson can be seen [here](http://gibsonenv.stanford.edu/database/). + ![spaces.png](images/spaces.png) -**Gibson Dataset Metadata:** Each space in the database has some metadata with the following attributes associated with it. The metadata is available in this [JSON file](https://raw.githubusercontent.com/StanfordVL/GibsonEnv/master/gibson/data/data.json). + +#### Dataset Metadata + +Each space in the database has some metadata with the following attributes associated with it. The metadata is available in this [JSON file](https://raw.githubusercontent.com/StanfordVL/GibsonEnv/master/gibson/data/data.json). ``` id # the name of the space, e.g. ""Albertville"" area # total metric area of the building, e.g. "266.125" sq. meters @@ -81,13 +101,10 @@ split_full+ # if the space is in train/val/test/none split of Full+ split_medium # if the space is in train/val/test/none split of Medium partition split_tiny # if the space is in train/val/test/none split of Tiny partition ``` -- Floor Number: Total number of floors in each model. We calculate floor numbers using distinctive camera locations. We use `sklearn.cluster.DBSCAN` to cluster these locations by height and set minimum cluster size to `5`. This means areas with at least `5` sweeps are treated as one single floor. This helps us capture small building spaces such as backyard, attics, basements. -- Area: Total floor area of each model. We calculate total floor area by summing up area of each floor. This is done by sampling point cloud locations based on floor height, and fitting a `scipy.spatial.ConvexHull` on sample locations. -- SSA: Specific surface area. The ratio of inner mesh surface and volume of convex hull of the mesh. This is a measure of clutter in the models: if the inner space is placed with large number of furnitures, objects, etc, the model will have high SSA. -- Navigation Complexity: The highest complexity of navigating between arbitrary points within the model. We sample arbitrary point pairs inside the model, and calculate `A∗` navigation distance between them. `Navigation Complexity` is equal to `A*` distance divide by `straight line distance` between the two points. We compute the highest navigation complexity for every model. Note that all point pairs are sample within the *same floor*. -- Subjective Attributes: We examine each model manually, and note the subjective attributes of them. This includes their furnishing style, house shapes, whether they have long stairs, etc. -**Gibson Dataset Format:** Each space in the database has its own folder. All the modalities and metadata for each space are contained in that folder. +#### Dataset Format + +Each space in the database has its own folder. All the modalities and metadata for each space are contained in that folder. ``` mesh_z_up.obj # 3d mesh of the environment, it is also associated with an mtl file and a texture file, omitted here floors.txt # floor height @@ -98,13 +115,26 @@ floor_trav_{}.png # top down views of traversable areas for each floor For the maps, each pixel represents 0.01m, and the center of the image correspond to `(0,0)` in the mesh, as well as in the pybullet coordinate system. +#### Dataset Metrics + + +**Floor Number** Total number of floors in each model. + +We calculate floor numbers using distinctive camera locations. We use `sklearn.cluster.DBSCAN` to cluster these locations by height and set minimum cluster size to `5`. This means areas with at least `5` sweeps are treated as one single floor. This helps us capture small building spaces such as backyard, attics, basements. + +**Area** Total floor area of each model. + +We calculate total floor area by summing up area of each floor. This is done by sampling point cloud locations based on floor height, and fitting a `scipy.spatial.ConvexHull` on sample locations. + +**SSA** Specific surface area. + +The ratio of inner mesh surface and volume of convex hull of the mesh. This is a measure of clutter in the models: if the inner space is placed with large number of furnitures, objects, etc, the model will have high SSA. + +**Navigation Complexity** The highest complexity of navigating between arbitrary points within the model. -## Download Matterport3D Scenes -What will you download? -- Matterport3D Dataset: 90 scenes (3.2GB) +We sample arbitrary point pairs inside the model, and calculate `A∗` navigation distance between them. `Navigation Complexity` is equal to `A*` distance divide by `straight line distance` between the two points. We compute the highest navigation complexity for every model. Note that all point pairs are sample within the *same floor*. -Please fill in this [form](http://dovahkiin.stanford.edu/matterport/public/MP_TOS.pdf) and send it to [matterport3d@googlegroups.com](mailto:matterport3d@googlegroups.com). Please put "use with iGibson simulator" in your email. +**Subjective Attributes** -You'll then recieve a python script via email in response. Run `python download_mp.py --task_data igibson -o .` with the received script to download the data (3.2GB). Afterwards, move each of the scenes to the path set in `your_installation_path/igibson/global_config.yaml` (default and recommended: `g_dataset: your_installation_path/igibson/data/g_dataset`). +We examine each model manually, and note the subjective attributes of them. This includes their furnishing style, house shapes, whether they have long stairs, etc. -Reference: [Matterport3D webpage](https://niessner.github.io/Matterport/). \ No newline at end of file diff --git a/docs/environments.md b/docs/environments.md index bdf6c9d6a..d9bfd6f01 100644 --- a/docs/environments.md +++ b/docs/environments.md @@ -52,9 +52,9 @@ We provide a few common termination conditions for robotics tasks. Most of the code can be found in [igibson/termination_conditions](https://github.com/StanfordVL/iGibson/tree/master/igibson/termination_conditions). #### Configs -To instantiate an **Environment**, we first need to create a YAML config file. It will specify parameters for the **Environment** (e.g. robot type, action frequency, etc), the **Sensors** (e.g. sensor types, image resolution, noise rate, etc), the **Task** (e.g. task type, goal distance range, etc), the **Reward Functions** (e.g. reward types, reward scale, etc) and the **Termination Conditions** (e.g. goal convergence threshold, time limit, etc). Exapmles of config files can be found here: [configs](https://github.com/StanfordVL/iGibson/tree/master/igibson/configs). +To instantiate an **Environment**, we first need to create a YAML config file. It will specify parameters for the **Environment** (e.g. robot type, action frequency, etc), the **Sensors** (e.g. sensor types, image resolution, noise rate, etc), the **Task** (e.g. task type, goal distance range, etc), the **Reward Functions** (e.g. reward types, reward scale, etc) and the **Termination Conditions** (e.g. goal convergence threshold, time limit, etc). Exapmles of config files can be found here: [examples/configs](https://github.com/StanfordVL/iGibson/tree/master/examples/configs). -Here is one example: [configs/turtlebot_nav.yaml](https://github.com/StanfordVL/iGibson/blob/master/igibson/configs/turtlebot_nav.yaml) +Here is one example: [examples/configs/turtlebot_point_nav.yaml](https://github.com/StanfordVL/iGibson/blob/master/examples/configs/turtlebot_point_nav.yaml) ```yaml # scene @@ -130,7 +130,8 @@ depth_noise_rate: 0.0 scan_noise_rate: 0.0 # visual objects -visible_target: true +visual_object_at_initial_target_pos: true +target_visual_object_visible_to_agent: false ``` @@ -182,8 +183,8 @@ Parameters of this config file are explained below: | laser_link_name | scan_link | the link name of the LiDAR sensor in the robot URDF file | | depth_noise_rate | 0.0 | noise rate for the depth camera. 0.1 means 10% of the pixels will be corrupted (set to 0.0) | | scan_noise_rate | 0.0 | noise rate for the LiDAR. 0.1 means 10% of the rays will be corrupted (set to laser_linear_range) | -| visible_target | true | whether to show visual markers for the target positions that are visible to the agent | -| visible_path | true | whether to show visual markers for the computed shortest path that are visible to the agent | +| visual_object_at_initial_target_pos | true | whether to show visual markers for the initial and target positions | +| target_visual_object_visible_to_agent | false | whether these visual markers are visible to the agents | ### Examples @@ -193,48 +194,29 @@ In this example, we show how to instantiate `iGibsonEnv` and how to step through - `reward`: a scalar that represents the current reward - `done`: a boolean that indicates whether the episode should terminate - `info`: a python dictionary for bookkeeping purpose -The code can be found here: [igibson/examples/environments/env_nonint_example.py](https://github.com/StanfordVL/iGibson/blob/master/igibson/examples/environments/env_nonint_example.py). - -If the execution fails with segfault 11, you may need to reduce texture scaling in the config file (igibson/configs/turtlebot_static_nav.yaml) to avoid out-of-memory error. +The code can be found here: [igibson/examples/demo/env_example.py](https://github.com/StanfordVL/iGibson/blob/master/igibson/examples/demo/env_example.py). ```python -import logging -import os -from sys import platform - -import yaml - -import igibson from igibson.envs.igibson_env import iGibsonEnv +from time import time +import igibson +import os from igibson.render.profiler import Profiler -from igibson.utils.assets_utils import download_assets, download_demo_data +import logging def main(): - """ - Creates an iGibson environment from a config file with a turtlebot in Rs (not interactive). - It steps the environment 100 times with random actions sampled from the action space, - using the Gym interface, resetting it 10 times. - """ - logging.info("*" * 80 + "\nDescription:" + main.__doc__ + "*" * 80) - # If they have not been downloaded before, download assets and Rs Gibson (non-interactive) models - download_assets() - download_demo_data() - config_filename = os.path.join(igibson.example_config_path, "turtlebot_static_nav.yaml") - config_data = yaml.load(open(config_filename, "r"), Loader=yaml.FullLoader) - # Reduce texture scale for Mac. - if platform == "darwin": - config_data["texture_scale"] = 0.5 - env = iGibsonEnv(config_file=config_data, mode="gui_interactive") + config_filename = os.path.join(igibson.example_config_path, 'turtlebot_demo.yaml') + env = iGibsonEnv(config_file=config_filename, mode='gui') for j in range(10): - logging.info("Resetting environment") env.reset() for i in range(100): - with Profiler("Environment action step"): + with Profiler('Environment action step'): action = env.action_space.sample() state, reward, done, info = env.step(action) if done: - logging.info("Episode finished after {} timesteps".format(i + 1)) + logging.info( + "Episode finished after {} timesteps".format(i + 1)) break env.close() @@ -244,4 +226,4 @@ if __name__ == "__main__": ``` #### Interactive Environments -In this example, we show how to instantiate `iGibsobEnv` with a fully interactive scene `Rs_int`. In this scene, the robot can interact with all the objects in the scene (chairs, tables, couches, etc). The code can be found here: [igibson/examples/environments/env_int_example.py](https://github.com/StanfordVL/iGibson/blob/master/igibson/examples/env_int_example.py). +In this example, we show how to instantiate `iGibsobEnv` with a fully interactive scene `Rs_int`. In this scene, the robot can interact with all the objects in the scene (chairs, tables, couches, etc). The code can be found here: [igibson/examples/demo/env_interactive_example.py](https://github.com/StanfordVL/iGibson/blob/master/igibson/examples/demo/env_interactive_example.py). \ No newline at end of file diff --git a/docs/examples.md b/docs/examples.md deleted file mode 100644 index 5d67aa725..000000000 --- a/docs/examples.md +++ /dev/null @@ -1,21 +0,0 @@ -# Code Examples - -In this section, we include a description of most of the code examples you can find under `igibson/examples/`. -There may be new examples since we wrote this, so check that folder! - -If you are interested in just getting started as an end-user, you only need check out `igibson/examples/environments`. - -If you are looking for examples of BEHAVIOR, the benchmark of household activities that uses iGibson, please check the BEHAVIOR repository at https://github.com/StanfordVL/behavior. - -- environments: how to instantiate iGibson environments with interactive or static scenes, optionally with a scene selector. -- learning: how to train RL policies for robot navigation using stable baselines 3, and how to save and replay demos of agents for imitation learning. -- objects: how to create, load, and place objects to predefined locations or using a logic sampler (e.g. onTop(A, B)), how to change texture as a function of the temperature, and how to generate the minimum volume bounding boxes of objects. -- object_states: how to change various objects states, including dusty, stained, (water sources) toggled on, (cleaning tool) soaked, sliced, and temprature, and how to save and reload object states. -- observations: how to generate different observation modalities such as RGB, depth, LiDAR, segmentation, etc. -- renderer: how to use the renderer directly, without the physics engine. -- robots: how to (keyboard) control robots with differential drive controllers, IK controllers and sampling-based motion planners. -- ros: how to run ROS with iGibson as if it is the real world. -- scenes: how to load interactive and non-interactive scenes, how to use domain randomization (of object models and/or texture), and how to create a tour video of the scenes. -- vr: how to use iGibson with VR. -- web_ui: how to start a web server that hosts iGibson environments. - diff --git a/docs/extended_states.md b/docs/extended_states.md deleted file mode 100644 index c6c89cb5a..000000000 --- a/docs/extended_states.md +++ /dev/null @@ -1,82 +0,0 @@ -# Extended States and Logic States - -### Extended States - -iGibson 2.0 is an object-oriented simulator: the simulator maintains and updates a list of objects with properties that change over-time. -Object classes are organized following the WordNet hierarchy. -Different to other simulators, iGibson 2.0 is not limited to kinematic properties of the objects (pose, velocity, acceleration, joint configurations for articulated objects), but it includes also a set of additional object-properties that we call "extended states". -The extended states are: - -#### Temperature - -Real continuous value per object. It can change if the object is close/inside a source or sink of heat, and the source/sink is active. -![extstates1.jpg](images/extstates1.jpg) - -#### Wetness level - -Integer value per object. It changes when in contact with a water dropplet. -![extstates2.jpg](images/extstates2.jpg) - -#### Cleanliness (Dustiness and Stain Level) - -In iGibson 2.0, objects can be initialized with visible dust or stain particles on its surface. The number of particles at initialization corresponds to a 100% level of dustiness, d, or stains, s, as we assume that dust/stain particles cannot be generated after initialization. As particles are cleaned, the level decreases proportionally to the number of particles removed, reaching a level of 0% when all particles have been cleaned. -![extstates3.jpg](images/extstates3.jpg) - -#### Toggled State -Some object categories in iGibson 2.0 can be toggled on and off. iGibson 2.0 maintains and updates an internal binary functional state for those objects. -![extstates4a.jpg](images/extstates4a.jpg) - -#### Sliced State -Many cooking activities require the agent to slice objects, e.g. food items. -In iGibson 2.0 some objects can be sliced into two halves and the state of the object will be updated. -![extstates4b.jpg](images/extstates4b.jpg) - - -## Logic States - -Based on the kinematic and extended states, iGibson 2.0 implements a list of logic predicates, checking conditions to indicate if a logic state is True or False. -The logic states implemented and checked by iGibson 2.0 include: -- InsideOf(o1,o2): Object o1 is inside of object o2 if we can find two orthogonal axes crossing at o1 center of mass that intersect o2 collision mesh in both directions. -- OnTopOf(o1,o2): Object o1 is on top of object o2 if o2 is in the list of objects InSameNegativeVerticalAxisObjs(o1) and o2 is in the list of objects InSamePositiveVerticalAxisObjs(o1) and InContactWith(o1, o2), where InSamePositive/NegativeVerticalAxisObjs(o1) is the list of objects in the same positive/negative vertical axis as o1 and InContactWith(o1, o2) is whether the two objects are in physical contact. -- NextTo(o1,o2): Object o1 is next to object o2 if o2 is in the list of objects InSameHorizontalPlaneObjs(o1) and l2(o1, o2) < tNextTo , where InSameHorizontalPlaneObjs(o1) is a list of objects in the same horizontal plane as o1, l2 is the L2 distance between the closest points of the two objects, and tNextTo is a distance threshold that is proportional to the average size of the two objects. -- InContactWith(o1,o2): Object o1 is in contact with o2 if their surfaces are in contact in at least one point, i.e., o2 is in the list of objects InContactObjs(o1). -- Under(o1,o2): Object o1 is under object o2 if o2 is in the list of objects InSamePositiveVerticalAxisObjs(o1) and o2 6 is in the list of objects InSameNegativeVerticalAxisObjs(o1). -- OnFloor(o1,o2): Object o1 is on the room floor o2 if InContactWith(o1, o2) and o2 is of Room type. -- Open(o): Any joints (internal articulated degrees of freedom) of object o are open. Only joints that are relevant to consider an object Open are used in the predicate computation, e.g. the door of a microwave but not the buttons and controls. To select the relevant joints, object models of categories that can be Open undergo an additional annotation that produces a RelevantJoints list. A joint is considered open if its joint state q is 5% over the lower limit, i.e. q > 0.05(qUpperLimit − qLowerLimit ) + qLowerLimit. -- Cooked(o): The temperature of object o was over the cooked threshold, Tcooked , and under the burnt threshold, Tburnt, at least once in the history of the simulation episode, i.e., Tcooked ≤ T max o < Tburnt. We annotate the cooked temperature Tcooked for each object category that can be Cooked. -- Burnt(o): The temperature of object o was over the burnt threshold, Tburnt, at least once in the history of the simulation episode, i.e., T max o ≥ Tburnt. We annotate the burnt temperature Tburnt for each object category that can be Burnt. -- Frozen(o): The temperature of object o is under the freezing threshold, Tfrozen , i.e., To ≤ Tfrozen . We assume as default freezing temperature Tfrozen = 0◦C, a value that can be adapted per object category and model. -- Soaked(o): The wetness level w of the object o is over a threshold, wsoaked , i.e., w ≥ wsoaked . The default value for the threshold is wsoaked = 1, (the object is soaked if it absorbs one or more droplets), a value that can be adapted per object category and model. -- Dusty(o): The dustiness level d of the object o is over a threshold, ddusty , i.e., d > ddusty . The default value for the threshold is ddusty = 0.5, (half of the dust particles have been cleaned), a value that can be adapted per object category and model. -- Stained(o): The stain level s of the object o is over a threshold, sstained , i.e., s > sstained . The default value for the threshold is sstained = 0.5, (half of the stain particles have been cleaned), a value that can be adapted per object category and model. -- ToggledOn(o): Object o is toggled on or off. It is a direct query of the iGibson 2.0 objects’ extended state TS, the toggled state. -- Sliced(o): Object o is sliced or not. It is a direct access of the iGibson 2.0 objects’ extended state SS, the sliced state. -- InFoVOfAgent(o): Object o is in the field of view of the agent, i.e., at least one pixel of the image acquired by the agent’s onboard sensors corresponds to the surface of o. -- InHandOfAgent(o): Object o is grasped by the agent’s hands (i.e. assistive grasping is activated on that object). -- InReachOfAgent(o): Object o is within dreach = 2 meters away from the agent. -- InSameRoomAsAgent(o): Object o is located in the same room as the agent. - - -We do not annotate all objects all extended states. -Objects are annotated with extended states that allow to query meaningful and useful logic states from them, e.g. temperature to query if food is cooked (or frozen or burned). -This doesn't mean a table does not have temperature, but it is not relevant for most robotics activities simulated in iGibson 2.0. -We annotate object classes with meaningful logic states that apply to them, and, possibly, the parameters to check these states. -The list of classes and states can be found in the BDDL repository ([list of object characteristics](https://github.com/sanjanasrivastava/BDDL/blob/master/utils/synsets_to_filtered_properties_pruned_igibson.json)). - -The characteristics that can be annotated include (additional extended state and parameters in parenthesis): - (e.g., the temperature is not necessary/relevant for non-food categories for most tasks of interest). -- cookable: Can be Cooked (MaxTemperature, Temperature) -- burnable: Can be Burnt (MaxTemperature, Temperature) -- freezable: Can be Frozen (Temperature) -- soakable: Can be Soaked (WetnessLevel) -- dustyable: Can be Dusty (DustinessLevel) -- stainable: Can be Stained (StainLevel) -- toggleable: Can be ToggledOn (ToggledState) -- sliceable: Can be Sliced (SlicedState) - -Additionally, some objects present characteristics to help changing the logic states of others (additional extended state in parenthesis): -- Is a 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