发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形机器人足地交互缺乏触觉感知的问题，提出TactileStep框架，通过足底压力感知与阶段感知奖励，实现更柔和着陆和稳定支撑，显著降低冲击力与噪声。
AI 中文摘要
人形机器人跑酷策略能够穿越各种地形，但任务完成可能掩盖了剧烈着陆、边缘接触和不稳定站立接触的挑战。人类通过触觉反馈自然调节足地交互，根据地形刚度调整接触顺应性。这凸显了人类与人形机器人之间的关键领域差距：大多数人体系统中缺乏丰富的触觉感知。我们通过TactileStep解决这一问题，这是一个可部署的触觉学习框架，将足底压力感知引入人形机器人运动控制，以实现更柔和的着陆和更稳定的支撑。TactileStep将触觉模拟与真实压力鞋垫对齐，使策略能够从硬件上可用的相同接触特征中学习。在训练过程中，我们利用触觉和运动线索识别不同的足部接触阶段，并应用阶段感知奖励，鼓励更安全的着陆和更稳定的站立。在仿真和Unitree G1人形机器人上跨多种地形进行评估，TactileStep相比强感知基线，将峰值着陆力降低高达48.8%，峰值A加权冲击噪声降低高达30.1分贝，同时将站立接触面积增加高达23.8%。
英文摘要
Humanoid parkour policies can traverse various terrains, but task completion may mask challenges of harsh landings, edge contacts, and unstable stance contacts. Humans naturally regulate foot-terrain interaction through tactile feedback, modulating contact compliance according to terrain stiffness. This highlights a key domain gap between humans and humanoid robots: the absence of rich tactile sensing in most humanoid systems. We address this problem with TactileStep, a deployable tactile learning framework that brings sole pressure sensing into humanoid locomotion control for softer touchdowns and more stable support. TactileStep aligns tactile simulation with the real pressure insole, allowing the policy to learn from the same contact features available on hardware. During training, we use tactile and motion cues to recognize different foot-contact phases and apply phase-aware rewards that encourage safer landing and more stable stance. Evaluated in simulation and on a Unitree G1 humanoid across diverse terrains, TactileStep reduces peak touchdown force by up to 48.8% and peak A-weighted impact noise by up to 30.1 dB over a strong perceptive baseline, while increasing stance contact area by up to 23.8%.
