Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control

TL;DR

Judo is an open-source Python package that simplifies the development, testing, and deployment of sampling-based model predictive control algorithms for robotics, featuring real-time simulation and a user-friendly interface.

Contribution

It provides a comprehensive, easy-to-use toolkit with robust algorithms, standardized benchmarks, and interactive tools to advance sampling-based MPC in robotics.

Findings

Achieves real-time performance on various hardware.

Facilitates rapid prototyping and evaluation.

Supports seamless simulation-to-hardware transfer.

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.