用触觉反馈提升软体四足机器人仿真实现真实行走
SENSE-STEP: Learning Sim-to-Real Locomotion for a Sensory-Enabled Soft Quadruped Robot
- 分阶段训练控制策略，融合本体与触觉感知
- 实机测试中平地速度提升41%，斜坡提速91%
- 触觉与惯性反馈显著增强稳定性，最高提升56%
由于高维动力学、执行器滞后及难以建模的接触交互，软体四足机器人的闭环运动控制仍具挑战，传统本体感觉对地面接触信息捕捉有限。本文提出一种基于学习的控制框架，针对配备气动驱动和触觉吸盘足的软体四足机器人，在仿真中通过分阶段训练，从基准步态逐步优化于随机环境条件。所获控制器将本体与触觉反馈映射为协调的气动驱动和吸盘指令，实现平坦与倾斜表面的闭环行走。在真实机器人上部署后，闭环策略相较开环基线，平地前进速度提升41%，5度斜坡上提升91%。消融实验进一步验证触觉力估计与惯性反馈在稳定运动中的作用，无感官反馈配置下性能最高下降56%。
原文摘要 · Abstract (English)
Robust closed-loop locomotion remains challenging for soft quadruped robots due to high-dimensional dynamics, actuator hysteresis, and difficult-to-model contact interactions, while conventional proprioception provides limited information about ground contact. In this paper, we present a learning-based control framework for a pneumatically actuated soft quadruped equipped with tactile suction-cup feet, and we validate the approach experimentally on physical hardware. The control policy is trained in simulation through a staged learning process that starts from a reference gait and is progressively refined under randomized environmental conditions. The resulting controller maps proprioceptive and tactile feedback to coordinated pneumatic actuation and suction-cup commands, enabling closed-loop locomotion on flat and inclined surfaces. When deployed on the real robot, the closed-loop policy outperforms an open-loop baseline, increasing forward speed by 41% on a flat surface and by 91% on a 5-degree incline. Ablation studies further demonstrate the role of tactile force estimates and inertial feedback in stabilizing locomotion, with performance improvements of up to 56% compared to configurations without sensory feedback.
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