让四足机器人靠触觉感知搬运不固定的圆柱体,无需固定装置。
LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing
- 用高密度触觉传感器覆盖机器人背部,实现大范围触觉感知。
- 在仿真中训练出能自适应调整步态的控制策略,可零样本迁移到真实世界。
- 可稳定搬运不同大小重量、表面材质的圆柱物,抗扰动能力强。
四足机器人在复杂地形上表现出色,但在动态物体交互任务中仍面临接触感知与控制难题。为此,我们提出 LocoTouch 系统,通过为四足机器人配备触觉传感,解决长距离搬运未固定圆柱体这一挑战性任务——传统方法通常需依赖定制安装或绑扎装置以保持稳定。为实现高效的大面积触觉感知,我们设计了一种高密度分布式触觉传感器,覆盖机器人全身背部。为有效利用触觉反馈进行控制,我们构建了具有高保真触觉信号的仿真环境,并采用两阶段学习流程训练触觉感知型运输策略。此外,设计新型奖励函数以促进对称、鲁棒且频率自适应的步态。训练完成后,LocoTouch 实现零样本迁移至真实世界,能够可靠运输多种尺寸、重量和表面特性的未固定圆柱体,在不平整地形上持续运行且具备强抗扰动能力。
原文摘要 · Abstract (English)
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.
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