A software framework integrating various imitation learning methods and benchmark environments for robotic manipulation.
Provides easy-to-use baselines for policy training, evaluation, and deployment.
RoboManipBaselines_VideoVer200.mp4
RoboManipBaselines_VideoVer100.mp4
Start collecting data in the MuJoCo simulation, train your model, and rollout the ACT policy in just a few steps!
📄 See the Quick Start Guide.
Follow our step-by-step Installation Guide to get set up smoothly.
We provide several powerful policy architectures for manipulation tasks:
- 🔹 MLP: Simple feedforward policy
- 🔹 SARNN: Sequence-aware RNN-based policy
- 🔹 ACT: Transformer-based imitation policy
- 🔹 MT-ACT: Multi-task Transformer-based imitation policy
- 🔹 Diffusion Policy: Diffusion-based behavior cloning policy
- 🔹 3D DiffusionPolicy: Diffusion-based policy using 3D point cloud observations
- 📂 Dataset List: Pre-collected expert demonstration datasets
- 🧠 Learned Parameters: Trained model checkpoints and configs
- 📄 Data Format: Description of the custom RMB data format used in RoboManipBaselines
Use your own teleop interface to collect expert data.
See Teleop Tools for more info.
- 🎮 Multiple SpaceMouse: Setup multiple SpaceMouse for high-degree-of-freedom robots
Explore diverse manipulation environments:
- 📚 Environment Catalog: Overview of all task environments
- 🔧 Env Setup: Installation guides per environment
- ✏️ How to Add a New Environment: Guide for adding a custom environment
- 🔅️ MuJoCo Tactile Sensor: Guide for using tactile sensors in MuJoCo environments
Check out Misc Scripts for standalone tools and utilities.
See Benchmarked Performance across environments and policies.
We welcome contributions!
Check out the Contribution Guide to get started.
This repository is licensed under the BSD 2-Clause License, unless otherwise stated.
Please check individual files or directories (especially third_party
and assets
) for specific license terms.
If you use RoboManipBaselines in your work, please cite us:
@software{RoboManipBaselines_GitHub2024,
author = {Murooka, Masaki and Motoda, Tomohiro and Nakajo, Ryoichi},
title = {{RoboManipBaselines}},
url = {https://github.com/isri-aist/RoboManipBaselines},
version = {1.0.0},
year = {2024},
month = dec,
}