Judo是一款易用的开源采样型模型预测控制工具包，助力机器人快速原型设计与部署。
Judo: A User-Friendly Open-Source Package for Sampling-Based Model Predictive Control
- 提供多种采样型MPC算法的稳定实现和标准化测试任务
- 支持从仿真到硬件的异步执行，实现实时性能
- 适合机器人研究者快速开发与交互式调参
近年来，并行仿真进展和成功机器人应用推动了采样型模型预测控制（MPC）的复兴。然而，为延续这一进展，机器人社区亟需统一的工具用于原型设计、评估与部署。我们提出Judo，一款旨在解决此需求的软件包。Judo通过提供常见采样型MPC算法的稳健实现和标准化基准任务，促进快速原型设计与评估；其接口简洁可扩展，支持控制器与任务定义；具备异步执行能力，便于从仿真向硬件迁移；并配备高度可定制的交互式图形界面，支持控制器的实时调参。尽管基于Python开发，但借助MuJoCo物理引擎后端，Judo在消费级与服务器级硬件上均实现了实时性能。代码见https://github.com/bdaiinstitute/judo。
原文摘要 · Abstract (English)
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.
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