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Diffusion Policy
A visuomotor policy approach using diffusion to generate robot actions.
Robot policies
Models / Research
RTWholeBodyMPPI · jrapudg
We present the first successful deployment of a whole-body sampling-based MPC system on a real-world quadruped robot. Our approach enables real-time, contact-rich locomotion and manipulation using…
Overview
We present the first successful deployment of a whole-body sampling-based MPC system on a real-world quadruped robot. Our approach enables real-time, contact-rich locomotion and manipulation using…
Publisher purpose: We present the first successful deployment of a whole-body sampling-based MPC system on a real-world quadruped robot. Our approach enables real-time, contact-rich locomotion and manipulation using a single control policy derived from Model-Predictive Path Integral (MPPI) control.
Review observation/action interfaces, checkpoints, robot embodiments and adaptation requirements. Repository code licensing does not automatically license weights or training data.
Project information
- Maintainer
- jrapudg
- Primary language
- Jupyter Notebook
- Repository code license
- MIT
- Model-weight license
- Not recorded
- Dataset license
- Not recorded
- Hardware design license
- Not recorded
- Maintenance
- Not archived in the last recorded metadata
- Last observed repository push
- 2025-05-18
Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.
Documentation sections
- Whole-Body MPPI
- MPPI explained:
- **Release Note**
- Contents
- Installation
- MuJoCo Simulation ONLY
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