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

Abstract

Sampling-based model predictive control (MPC) is experiencing a resurgence in robotics following both recent hardware successes and advancements in parallelized physics simulation. However, to build on this progress, the robotics community needs to develop shared tools for prototyping, benchmarking, 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 a comprehensive suite of benchmark tasks. It 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 the high-level library is written in Python, judo leverages MuJoCo as its physics backend to achieve real-time performance. We present example benchmarking results using judo to compare standard sampling-based controllers across its tasks. We also provide real-world case studies in deploying judo on hardware for two contact-rich tasks: in-hand cube rotation and quadrupedal loco-manipulation. Code at https://github.com/bdaiinstitute/judo.