The field of robot learning is experiencing remarkable advances driven by reinforcement learning, imitation learning, and the emergence of foundation models. Alongside the growing momentum of open-source software development in these areas, we are now witnessing an increasing number of open-source robotic hardware platforms being shared with the community—from quadrupeds to dual-arm manipulators and humanoids.
This workshop aims to bring together researchers and developers who are actively involved in the design, development, and dissemination of open-source robotic hardware. The central goal is to foster an open, collaborative community where knowledge and experiences around hardware and its integration with robot learning can be shared across groups.
We will structure the discussion around the following key questions and topics:
What kinds of actuators, sensors, and hardware designs are currently being used in open-source robot platforms?
What are the best practices for integrating open-source hardware with modern robot learning frameworks?
How should licensing and intellectual property considerations be handled in open-source hardware projects?
Which simulators and software environments are most compatible and useful for learning-oriented robotics research?
How can hardware developers communicate capabilities and limitations effectively to the learning community?
Are there sustainable models for monetization or community support in open-source hardware development?
This workshop will benefit the robot learning community by lowering the barrier to entry for experimenting with real-world robotics and by promoting the sharing of physical platforms in the same way open-source software has accelerated algorithmic innovation. The intended audience includes researchers and practitioners working on robot learning, robotics hardware development, open-source projects, and simulation environments. Presenters and panelists will be drawn from communities involved in open-source robot hardware development, reinforcement learning, imitation learning, foundation model-based control, and robotics systems integration.
By building bridges between hardware developers and the robot learning community, this workshop seeks to catalyze new collaborations and accelerate innovation in embodied AI.
