Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
Abstract
Recent advancements in parallel simulation and successful robotic applications are spurring a resurgence in sampling-based model predictive control. To build on this progress, however, the robotics community needs common tooling for prototyping, evaluating, and deploying sampling-based controllers. We introduce Judo, a software package designed to address this need. To facilitate rapid prototyping and evaluation, Judo provides robust implementations of common sampling-based MPC algorithms and standardized benchmark tasks. It further emphasizes usability with simple but extensible interfaces for controller and task definitions, asynchronous execution for straightforward simulation-to-hardware transfer, and a highly customizable interactive GUI for tuning controllers interactively. While written in Python, the software leverages MuJoCo as its physics backend to achieve real-time performance, which we validate across both consumer and server-grade hardware. Code at https://github.com/bdaiinstitute/judo.
Cite
@article{arxiv.2506.17184,
title = {Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control},
author = {Albert H. Li and Brandon Hung and Aaron D. Ames and Jiuguang Wang and Simon Le Cleac'h and Preston Culbertson},
journal= {arXiv preprint arXiv:2506.17184},
year = {2025}
}
Comments
Accepted at the 2025 RSS Workshop on Fast Motion Planning and Control in the Era of Parallelism. 5 Pages