一个模型控制所有腿式机器人，实现跨形态零样本迁移。
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
- 用抽象控制器+形态无关编码器，统一学习多形态运动策略。
- 在仿真和真实世界中成功迁移至未见过的机器人平台。
- 为足式机器人奠定基础模型可能，适合机器人通用控制研究者。
深度强化学习在稳健的足式运动中取得顶尖成果。尽管存在多种足式平台如四足、人形和六足机器人，但目前仍缺乏一个能轻松高效控制所有形态的统一学习框架，且难以实现对未见机器人形态的零样本或少样本迁移。本文提出统一机器人形态架构URMA，将端到端多任务强化学习引入足式机器人领域，使学习到的策略可控制任意类型的机器人形态。核心思路是让网络学习一种抽象运动控制器，通过形态无关的编码器与解码器，在不同形态间无缝共享。该灵活架构或可成为足式机器人运动基础模型的第一步。实验表明，URMA可在多个形态上学习运动策略，并可轻松迁移至仿真与现实世界中的未见机器人平台。
原文摘要 · Abstract (English)
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.
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