让仿人机器人在复杂地形上自然行走，还能自由切换步态。
MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains
- 用潜空间残差专家混合模型+多判别器训练策略
- 实现在复杂地形上稳定行走，步态切换无缝自然
- 适合研究仿人机器人运动控制与真实场景应用
仿人机器人已通过强化学习（RL）实现稳健的行走能力。为获得类人行为，现有方法在RL框架中引入人类动作追踪或运动先验，但这些方法仅限于仅靠本体感知在平坦地形上运行，难以在复杂地形上实现类人步态。本文提出一种新框架：使用带有多个判别器的潜在残差专家混合模型训练RL策略，可在具备外感受信息（如深度相机）条件下，实现对复杂地形的可控类人步态行走。该方法采用两阶段训练：首先利用深度相机教会策略穿越复杂地形；随后实现基于步态指令的类人步态模式切换。我们还设计了步态奖励机制，用于调节类人行为特征，如机器人基座高度。仿真与真实世界实验表明，本框架在复杂地形上表现优异，并能实现多种类人步态间的平滑过渡。
原文摘要 · Abstract (English)
Humanoid robots have demonstrated robust locomotion capabilities using Reinforcement Learning (RL)-based approaches. Further, to obtain human-like behaviors, existing methods integrate human motion-tracking or motion prior in the RL framework. However, these methods are limited in flat terrains with proprioception only, restricting their abilities to traverse challenging terrains with human-like gaits. In this work, we propose a novel framework using a mixture of latent residual experts with multi-discriminators to train an RL policy, which is capable of traversing complex terrains in controllable lifelike gaits with exteroception. Our two-stage training pipeline first teaches the policy to traverse complex terrains using a depth camera, and then enables gait-commanded switching between human-like gait patterns. We also design gait rewards to adjust human-like behaviors like robot base height. Simulation and real-world experiments demonstrate that our framework exhibits exceptional performance in traversing complex terrains, and achieves seamless transitions between multiple human-like gait patterns.
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