Publication
One Policy to Run Them All: Towards an End-to-end Learning Approach to Multi-Embodiment Locomotion
N. Bohlinger; G. Czechmanowski; M. Krupka; P. Kicki; K. Walas; Jan Peters; D. Tateo
In: RSS 2024 Workshop on Embodiment-Aware Robot Learning. Robotics: Science and Systems (RSS-2024), RSS, 2024.
Abstract
The field of legged robotics is still missing a single learning framework that can control different embodiments— such as quadruped, humanoids, and hexapods simultaneously and possibly transfer, zero or few-shot, to unseen robot embodiments. To close this gap, we introduce URMA, the Unified RobotMorphology Architecture. Our framework brings the end-to-end Multi-Task Reinforcement Learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world.