Tac4Loco：学习用于人形机器人运动的时空足底压力表征
Tac4Loco: Learning Spatiotemporal Plantar Pressure Representations for Humanoid Locomotion
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- The University of Hong Kong（香港大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出Tac4Loco框架，通过构建拓扑保持的序数表征提取足底压力时空特征，结合增强本体感知训练非对称演员-评论家策略，提升人形机器人在复杂地形的运动性能并实现零样本部署。
AI中文摘要：
人形机器人需在复杂地形中移动，足底支撑会因脚放置误差、地面特性及瞬态动力学发生显著变化。为实现稳健运动，机器人需适应不平地形与不确定的脚-地交互。现有运动策略主要依赖本体感知或外感受地形感知：前者仅提供足底支撑的间接证据，后者在触地前预测接触条件但无法实时观测实际支撑。尽管部分研究将足底接触作为辅助感知，但主要依赖汇总统计，忽略了足底压力的空间拓扑结构——该结构可更直接地表征实际接触状态。为填补这一空白，本文提出Tac4Loco，一种将多阵列足底压力作为人形机器人运动直接反馈的触觉感知框架。我们构建拓扑保持的序数表征，将模拟与物理传感器信号映射到共享观测空间，采用双分支编码器提取其空间与时间表征。随后，将学习到的时空特征与增强本体感知（含地形估计线索）融合，输入非对称演员-评论家架构进行策略学习。大量仿真与真实实验表明，该方法在倾斜、局部、不对称及支撑变化的地形上，跟踪性能与支撑适应性均有提升。我们进一步验证其在未见过的柔顺与非结构化地形（包括泡沫平台与砾石路）上的零样本部署能力。所有代码与实验配置将作为开源发布，以促进可复现性。
英文摘要：
Humanoid robots are expected to traverse complex terrains, where the plantar support may vary dramatically due to foot placement errors, ground properties, and transient dynamics. To achieve robust locomotion, the robots are required to adapt to uneven terrain and uncertain foot--ground interactions. Existing locomotion policies rely primarily on proprioception or exteroceptive terrain perception, where the former provides only indirect evidence of plantar support, while the latter predicts contact conditions before touchdown but cannot observe the actual support in real-time. Although some studies incorporate plantar contacts as an auxiliary perception, they rely mainly on summary statistics, overlooking the spatial topology of plantar pressure, which provides a more direct characterization of the realized contact state. To bridge this gap, we present Tac4Loco, a tactile-perceptive framework that incorporates multi-array plantar pressure as direct feedback for humanoid locomotion. We formulate a topology-preserving ordinal representation to map simulated and physical sensor signals into a shared observation space, with a dual-branch encoder for extracting their spatial and temporal representations. Subsequently, the learned spatiotemporal features are integrated with augmented proprioception including terrain estimation cues, and provided to an asymmetric actor-critic architecture for policy learning. Extensive simulation and real-world experiments demonstrate improved tracking performance and support adaptation on terrains with inclined, partial, asymmetric, and changing support. We further demonstrate its zero-shot deployment on unseen compliant and unstructured terrains, including a foam platform and a gravel road. All code and experimental configurations will be released as open-source to facilitate reproducibility.
