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

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

arXiv · 2506.21628

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

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

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Abstract

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.

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BibTeX RIS

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. 2025-06-24. Ark: An Open-source Python-based Framework for Robot Learning. https://arxiv.org/abs/2506.21628

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