LocoTouch：利用触觉感知学习四足机器人动态运输
LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing
AI总结：
针对四足机器人动态物体交互中接触感知与控制的难题，提出配备高密度背部触觉传感器的LocoTouch系统，通过两阶段学习与新型奖励函数训练触觉感知运输策略，实现零样本现实迁移，可稳定运输多样无固定圆柱物体且鲁棒性强。
摘要：
四足机器人已在穿越复杂地形方面展现出卓越的敏捷性与鲁棒性，但在动态物体交互任务中表现不佳，这类任务需要精确感知并控制接触状态。为弥补这一差距，我们提出LocoTouch系统，为四足机器人配备触觉感知能力，以解决该领域中一项极具挑战性的任务：无固定圆柱形物体的长距离运输——这类任务通常需要定制化的安装或紧固机构来维持稳定性。为实现高效的大面积触觉感知，我们设计了一款覆盖机器人整个背部的高密度分布式触觉传感器。为有效利用触觉反馈进行机器人控制，我们开发了一个具备高保真触觉信号的仿真环境，并采用两阶段学习流程训练具备触觉感知能力的运输策略。此外，我们设计了一种新型奖励函数，以促进鲁棒、对称且频率自适应的运动步态。经过仿真训练后，LocoTouch可零样本迁移至现实世界，能够可靠运输各种尺寸、重量及表面特性的无固定圆柱形物体，且在长距离、不平坦地形及剧烈扰动下仍保持鲁棒性。
英文摘要：
Quadrupedal robots have demonstrated remarkable agility and robustness in traversing complex terrains. However, they struggle with dynamic object interactions, where contact must be precisely sensed and controlled. To bridge this gap, we present LocoTouch, a system that equips quadrupedal robots with tactile sensing to address a particularly challenging task in this category: long-distance transport of unsecured cylindrical objects, which typically requires custom mounting or fastening mechanisms to maintain stability. For efficient large-area tactile sensing, we design a high-density distributed tactile sensor that covers the entire back of the robot. To effectively leverage tactile feedback for robot control, we develop a simulation environment with high-fidelity tactile signals, and train tactile-aware transport policies using a two-stage learning pipeline. Furthermore, we design a novel reward function to promote robust, symmetric, and frequency-adaptive locomotion gaits. After training in simulation, LocoTouch transfers zero-shot to the real world, reliably transporting a wide range of unsecured cylindrical objects with diverse sizes, weights, and surface properties. Moreover, it remains robust over long distances, on uneven terrain, and under severe perturbations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
