One Policy to Run Them All: Towards an End-to-end Learning Approach to Multi-Embodiment Locomotion

Nico Bohlinger, Grzegorz Czechmanowski, Maciej Piotr Krupka, Piotr Kicki, Krzysztof Walas, Jan Peters, Davide Tateo

Keywords: Locomotion, Reinforcement Learning, Multi-embodiment Learning

TL;DR: We propose a neural network architecture that can learn locomotion over multiple legged robot embodiments and morphologies

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 Robot Morphology 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.

Submission Number: 7