RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

This is the official RoboCasa codebase. Please refer to the accompanying paper and project website for additional information.

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Installation

RoboCasa works across all major computing platforms. The easiest way to set up is through the Anaconda package management system. Follow the instructions below to install:

1. Set up conda environment:

   conda create -c conda-forge -n robocasa python=3.9

2. Activate conda environment:

   conda activate robocasa

3. Clone and setup robosuite dependency (important: use the robocasa_v0.1 branch!):

   git clone https://github.com/ARISE-Initiative/robosuite -b robocasa_v0.1
   cd robosuite
   pip install -e .

4. Clone and setup this repo:

   cd ..
   git clone https://github.com/robocasa/robocasa
   cd robocasa
   pip install -e .

5. Install the package and download assets:

   conda install -c numba numba -y
   python robocasa/scripts/download_kitchen_assets.py   # Caution: Assets to be downloaded are around 5GB.
   python robocasa/scripts/setup_macros.py              # Set up system variables.

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Quick start

(Mac users: for these scripts, prepend the "python" command with "mj": mjpython ...)

Explore kitchen layouts and styles

Explore kitchen layouts (G-shaped, U-shaped, etc) and kitchen styles (mediterranean, industrial, etc):

python -m robocasa.demos.demo_kitchen_scenes

Play back sample demonstrations of tasks

Select a task and play back a sample demonstration for the selected task:

python -m robocasa.demos.demo_tasks

Explore library of 2500+ objects

View and interact with both human-designed and AI-generated objects:

python -m robocasa.demos.demo_objects

Note: by default this demo shows objaverse objects. To view AI-generated objects, add the flag --obj_types aigen.

Teleoperate the robot

Control the robot directly, either through a keyboard controller or spacemouse. This script renders the robot semi-translucent in order to minimize occlusions and enable better visibility.

python -m robocasa.demos.demo_teleop

Note: If using spacemouse: you may need to modify the product ID to your appropriate model, setting SPACEMOUSE_PRODUCT_ID in robocasa/macros_private.py.

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Datasets

RoboCasa comes with a large selection of demonstrations to faciliate training agents. This includes human trajectories and machine-generated trajectories from MimicGen. At this time we are releasing human datasets and plan to release MimicGen datasets in the near future.

Here are a few ways to download the datasets:

python -m robocasa.scripts.download_datasets                                            # downloads all datasets
python -m robocasa.scripts.download_datasets --tasks PnPCounterToCab ArrangeVegetables  # downloads datasets for specific tasks
python -m robocasa.scripts.download_datasets --overwrite                                # overwrites existing datasets
python -m robocasa.scripts.download_datasets --ds_types human_raw                       # lite download: download human datasets without images

By default, all datasets are stored under datasets/ in the root robocasa directory.

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Policy Learning

Refer to our policy learning repo to train and evaluate policies on our datasets.

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Key files

• robocasa/demos/: interactive scripts to explore tasks and environments (see quick start section for details)
• robocasa/environments/kitchen/: all implementations for kitchen tasks
  • kitchen.py: base class from which all kitchen tasks extend from
  • single_stage/: implementations of all single-stage tasks
  • multi_stage/: implementations of all multi-stage (aka "composite") tasks
• robocasa/scripts/: utility scripts
  • collect_demos.py: collect demonstration trajectories for any task and environment
  • download_kitchen_assets.py: downloads all assets. called automatically during setup in setup.sh
  • download_datasets.py: downloads datasets (see datasets section for details)
• robocasa/models/assets/: assets and implementations for objects and environment fixtures
  • assets/objects/: raw assets for all objects
  • assets/fixtures/: raw assets for all environment fixtures
  • assets/kitchen_layouts/: blueprints for kitchen layouts and designs
  • objects/kitchen_objects.py: registry for all object categories and groups
  • objects/fixtures/: implementations of all fixture classes

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Citation

@inproceedings{robocasa2024,
  title={RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots},
  author={Soroush Nasiriany and Abhiram Maddukuri and Lance Zhang and Adeet Parikh and Aaron Lo and Abhishek Joshi and Ajay Mandlekar and Yuke Zhu},
  booktitle={Robotics: Science and Systems},
  year={2024}
}