用对抗性运动先验让奇装异服的机器人稳稳走路
Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
- 用对抗性运动先验学习自然动作，兼顾美观与稳定
- 在头部占16%体重、行动受限下实现稳定站立行走
- 适合做演出机器人的设计者和强化学习研究者
我们为一款面向娱乐应用的定制人形机器人Cosmo设计了基于强化学习的行走系统。与传统人形机器人不同，娱乐机器人因美学设计带来独特挑战：Cosmo具有过大的头部（占总质量16%）、感知能力有限，且外壳保护装置严重限制动作范围。为此，我们采用对抗性运动先验（AMP）技术，使机器人在保持物理稳定的同时学会自然流畅的动作。通过定制域随机化方法和专门设计的奖励结构，确保仿真到现实的迁移安全，保护昂贵硬件。实验表明，尽管存在极端质量分布和运动约束，AMP仍能生成稳定的站立与行走行为。该结果为美学与功能兼具的机器人设计提供了可行路径，证明学习方法可有效适应审美驱动的设计约束。
原文摘要 · Abstract (English)
We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, entertainment robots present unique challenges due to aesthetic-driven design choices. Cosmo embodies these with a disproportionately large head (16% of total mass), limited sensing, and protective shells that considerably restrict movement. To address these challenges, we apply Adversarial Motion Priors (AMP) to enable the robot to learn natural-looking movements while maintaining physical stability. We develop tailored domain randomization techniques and specialized reward structures to ensure safe sim-to-real, protecting valuable hardware components during deployment. Our experiments demonstrate that AMP generates stable standing and walking behaviors despite Cosmo's extreme mass distribution and movement constraints. These results establish a promising direction for robots that balance aesthetic appeal with functional performance, suggesting that learning-based methods can effectively adapt to aesthetic-driven design constraints.
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