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.

翻译：对于软体四足机器人而言，由于其高维动力学特性、执行器迟滞效应以及难以建模的接触交互作用，实现鲁棒的闭环运动控制仍具挑战；而传统的本体感知仅能提供有限的地面接触信息。本文提出一种基于学习的控制框架，应用于配备触觉吸盘式足部的气动软体四足机器人，并通过物理硬件实验验证了该方法的有效性。控制策略在仿真环境中通过分阶段学习流程进行训练：该流程从参考步态出发，在随机化环境条件下逐步优化策略。最终生成的控制器能够将本体感知信号与触觉反馈映射为协调的气动执行指令与吸盘控制指令，从而实现在平坦及倾斜表面的闭环运动。当部署至真实机器人时，该闭环策略在性能上超越了开环基准方案：在平坦表面上将前进速度提升了41%，在5度斜坡上提升了91%。消融研究进一步揭示了触觉力估计与惯性反馈对运动稳定性的作用——相较于无传感反馈的配置，采用完整传感反馈的系统性能提升最高可达56%。