Project Page | arXiv

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.

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: Recurrent policy for sequential data
• 🔹 ACT: Transformer-based action chunking policy
• 🔹 MT-ACT: Multi-task Transformer-based imitation policy
• 🔹 Diffusion Policy: Diffusion-based imitation policy
• 🔹 3D Diffusion Policy: Diffusion-based policy with 3D point cloud input
• 🔹 Flow Policy: Flow-matching-based policy with 3D point cloud input
• 🔹 ManiFlow Policy: Flow-matching and consistency-based policy with 2D/3D vision
• 🔹 pi0: Flow-matching-based policy with a vision-language model backbone (PaliGemma)
• 🔹 GR00T: Diffusion Transformer-based policy with a vision-language model backbone (Eagle-2)

• 📂 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
• 🪄 Point Cloud Preprocessing: Data preprocessing for 3D point cloud policies

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 our paper:
arXiv | Hugging Face:

@article{RoboManipBaselines_Murooka_2025,
  title={RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments},
  author={Murooka, Masaki and Motoda, Tomohiro and Nakajo, Ryoichi and Oh, Hanbit and Makihara, Koshi and Shirai, Keisuke and Ogata, Tetsuya and Domae, Yukiyasu},
  journal={arXiv preprint arXiv:2509.17057},
  year={2025}
}