Metadata-Version: 2.4
Name: praxis-robocasa
Version: 1.0.0
Summary: RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
Home-page: https://github.com/Chaoqi-LIU/robocasa
Author: Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri, Yuke Zhu
Author-email: soroush@cs.utexas.edu
Maintainer: Chaoqi Liu
Maintainer-email: liuchaoqi730@gmail.com
Project-URL: Source, https://github.com/Chaoqi-LIU/robocasa
Project-URL: Maintainer Website, https://chaoqi-liu.com
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License-File: LICENSE
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<h1 align="center">RoboCasa</h1>
<img src="docs/images/readme.webp" width="100%" />

**RoboCasa** is a large-scale simulation framework for training generally capable robots to perform everyday tasks. It was [originally released](https://robocasa.ai/assets/robocasa_rss24.pdf) in 2024 by UT Austin researchers. The latest iteration, **RoboCasa365**, builds upon the original release with significant new functionalities to support large-scale training and benchmarking in sim. Four pillars underlie RoboCasa365:
- **Diverse tasks**: 365 tasks created with the guidance of large language models
- **Diverse assets**: including 2,500+ kitchen scenes and 3,200+ 3D objects
- **High-quality demonstrations**: including 600+ hours of human demonstrations in addition to 1,600+ hours of robot datasets created with automated trajectory tools
- **Benchmarking support**: popular policy learning methods including Diffusion Policy, pi, and GR00T, plus user-submitted models on the [leaderboard](https://robocasa.ai/leaderboard.html)


This guide contains information about installation and setup. Please refer to the following resources for additional information:

[**[Home page]**](https://robocasa.ai) &ensp; [**[Documentation]**](https://robocasa.ai/docs/introduction/overview.html) &ensp; [**[RoboCasa365 Paper]**](https://robocasa.ai/assets/robocasa365_iclr26.pdf) &ensp; [**[Original RoboCasa Paper]**](https://robocasa.ai/assets/robocasa_rss24.pdf) &ensp; [**[Leaderboard]**](https://robocasa.ai/leaderboard.html)

-------
## Installation
RoboCasa works across all major computing platforms. The easiest way to set up is through the [Anaconda](https://www.anaconda.com/) package management system. Follow the instructions below to install:
1. Set up conda environment:

   ```sh
   conda create -c conda-forge -n robocasa python=3.11
   ```
2. Activate conda environment:
   ```sh
   conda activate robocasa
   ```
3. Clone and setup robosuite dependency (**important: use the master branch!**):

   ```sh
   git clone https://github.com/ARISE-Initiative/robosuite
   cd robosuite
   pip install -e .
   ```
4. Clone and setup this repo:

   ```sh
   cd ..
   git clone https://github.com/robocasa/robocasa
   cd robocasa
   pip install -e .
   pip install pre-commit; pre-commit install           # Optional: set up code formatter.

   (optional: if running into issues with numba/numpy, run: conda install -c numba numba=0.56.4 -y)
   ```
5. Install the package and download assets:
   ```sh
   python -m robocasa.scripts.setup_macros              # Set up system variables.
   python -m robocasa.scripts.download_kitchen_assets   # Caution: Assets to be downloaded are around 10GB.
   ```

-------
## Basic Usage

### Gym wrapper
You can create environments using gym wrappers and run rollouts:
```py
import gymnasium as gym
import robocasa
from robocasa.utils.env_utils import run_random_rollouts

env = gym.make(
    "robocasa/PickPlaceCounterToCabinet",
    split="pretrain", # use 'pretrain' or 'target' kitchen scenes and objects
    seed=0 # seed environment as needed. set seed=None to run unseeded
)

# run rollouts with random actions and save video
run_random_rollouts(
    env, num_rollouts=3, num_steps=100, video_path="/tmp/test.mp4"
)
```

### Play back sample demonstrations of tasks
**(Mac users: for these scripts, prepend the "python" command with "mj": `mjpython ...`)**

Select a task and play back a sample demonstration for the selected task:
```
python -m robocasa.demos.demo_tasks
```

### Explore kitchen scenes
Explore 2500+ kitchen scenes:
```
python -m robocasa.demos.demo_kitchen_scenes
```

### 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`.

-------
## Tasks, datasets, policy learning, and additional use cases
Please refer to the [documentation page](https://robocasa.ai/docs/introduction/overview.html) for information about tasks, datasets, benchmarking, and more.

-------
## Releases
* [2/18/2026] **v1.0**: RoboCasa365 release, with 365 tasks, 2500+ kitchen scenes, 2200+ hours of robot demonstration data, and benchmarking support.
* [10/31/2024] **v0.2**: using RoboSuite `v1.5` as the backend, with improved support for custom robot composition, composite controllers, more teleoperation devices, photo-realistic rendering.

-------
## License
Code: [MIT License](https://opensource.org/license/mit)

Assets and Datasets: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)

-------
## Citation

**RoboCasa365:**

```bibtex
@inproceedings{robocasa365,
  title={RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots},
  author={Soroush Nasiriany and Sepehr Nasiriany and Abhiram Maddukuri and Yuke Zhu},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026}
}
```

**RoboCasa (Original Release):**

```bibtex
@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 (RSS)},
  year={2024}
}
