Ark：一个基于Python的开源机器人学习框架

Ark: An Open-source Python-based Framework for Robot Learning

June 24, 2025

作者: Magnus Dierking, Christopher E. Mower, Sarthak Das, Huang Helong, Jiacheng Qiu, Cody Reading, Wei Chen, Huidong Liang, Huang Guowei, Jan Peters, Quan Xingyue, Jun Wang, Haitham Bou-Ammar

cs.AI

摘要

机器人技术已在硬件方面取得了显著进展——从DARPA的城市与机器人挑战赛到首届人形机器人拳击锦标赛——然而，商业自主性仍落后于机器学习的进步。一个主要瓶颈在于软件：当前的机器人技术栈要求陡峭的学习曲线、深厚的C/C++专业知识、分散的工具链以及复杂的硬件集成，这与推动现代人工智能发展的以Python为核心、文档完善的生态系统形成鲜明对比。为此，我们推出了ARK，一个开源、优先采用Python的机器人框架，旨在弥合这一差距。ARK提供了一个类似Gym的环境接口，使用户能够收集数据、进行预处理，并利用最先进的模仿学习算法（如ACT、扩散策略）训练策略，同时在高保真模拟与实体机器人之间无缝切换。其轻量级的客户端-服务器架构支持网络化的发布-订阅通信，而可选的C/C++绑定确保了在需要时的实时性能。ARK配备了可复用的模块，涵盖控制、SLAM、运动规划、系统辨识和可视化，并原生支持ROS互操作性。详尽的文档和案例研究——从操作到移动导航——展示了快速原型设计、轻松的硬件更换，以及可与主流机器学习工作流相媲美的端到端流程。通过将机器人技术与人工智能实践统一在Python这一共同平台下，ARK降低了入门门槛，加速了自主机器人的研究与商业部署。

English

Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major bottleneck is software: current robot stacks demand steep learning curves, low-level C/C++ expertise, fragmented tooling, and intricate hardware integration, in stark contrast to the Python-centric, well-documented ecosystems that propelled modern AI. We introduce ARK, an open-source, Python-first robotics framework designed to close that gap. ARK presents a Gym-style environment interface that allows users to collect data, preprocess it, and train policies using state-of-the-art imitation-learning algorithms (e.g., ACT, Diffusion Policy) while seamlessly toggling between high-fidelity simulation and physical robots. A lightweight client-server architecture provides networked publisher-subscriber communication, and optional C/C++ bindings ensure real-time performance when needed. ARK ships with reusable modules for control, SLAM, motion planning, system identification, and visualization, along with native ROS interoperability. Comprehensive documentation and case studies-from manipulation to mobile navigation-demonstrate rapid prototyping, effortless hardware swapping, and end-to-end pipelines that rival the convenience of mainstream machine-learning workflows. By unifying robotics and AI practices under a common Python umbrella, ARK lowers entry barriers and accelerates research and commercial deployment of autonomous robots.