Ark: An Open-source Python-based Framework for Robot Learning
Authors: name: Magnus Dierking, name: Christopher E. Mower, name: Sarthak Das, name: Huang Helong, name: Jiacheng Qiu, name: Cody Reading, name: Wei Chen, name: Huidong Liang, name: Huang Guowei, name: Jan Peters, name: Quan Xingyue, name: Jun Wang, name: Haitham Bou-Ammar

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.