Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913904613916672 |
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| author | Li, Albert H. Hung, Brandon Ames, Aaron D. Wang, Jiuguang Cleac'h, Simon Le Culbertson, Preston |
| author_facet | Li, Albert H. Hung, Brandon Ames, Aaron D. Wang, Jiuguang Cleac'h, Simon Le Culbertson, Preston |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control Li, Albert H. Hung, Brandon Ames, Aaron D. Wang, Jiuguang Cleac'h, Simon Le Culbertson, Preston Robotics Systems and Control 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. |
| title | Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2506.17184 |