Ark: An Open-source Python-based Framework for Robot Learning
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.
686261739e7509383d29ac6e
Magnus Dierking
686261739e7509383d29ac6f
Christopher E. Mower
686261739e7509383d29ac70
Sarthak Das
686261739e7509383d29ac71
Huang Helong
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Jiacheng Qiu
686261739e7509383d29ac73
Cody Reading
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Wei Chen
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Huidong Liang
686261739e7509383d29ac76
Huang Guowei
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Jan Peters
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Quan Xingyue
686261739e7509383d29ac79
Jun Wang
686261739e7509383d29ac7a
Haitham Bou-Ammar
Ark: An Open-source Python-based Framework for Robot Learning
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.
ARK is an open-source Python-first framework that integrates modern imitation-learning algorithms and seamless simulation-physical robot interactions to simplify robotics development and deployment.
Gym-style environment interface imitation-learning algorithms ACT Diffusion Policy lightweight client-server architecture publisher-subscriber communication reusable modules control SLAM motion planning system identification visualization native ROS interoperability end-to-end pipelines