Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. 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. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. 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.

本研究解决了缺乏统一学习框架以控制各种腿部机器人形态的问题。我们提出的URMA框架通过端到端的多任务强化学习，使得学习到的策略能够灵活控制任意机器人形态，并可以在新型机器人平台上进行快速转移。实验结果表明，URMA在多样形态上学习的步态策略具有良好的迁移效果，对未来腿部机器人步态控制具有重要潜在影响。

一项政策统筹所有：端到端学习方法实现多形态步态控制