SENSE-STEP：为具有感知能力的软四足机器人学习仿真到现实的运动控制
SENSE-STEP: Learning Sim-to-Real Locomotion for a Sensory-Enabled Soft Quadruped Robot
AI总结：
本文提出了一种基于学习的控制框架，通过仿真训练使软四足机器人在现实环境中实现更稳定的闭环运动，显著提升了在不同地形上的运动速度和稳定性。
摘要：
对于软四足机器人来说，鲁棒的闭环运动控制仍然具有挑战性，因为存在高维动力学、执行器滞后以及难以建模的接触相互作用，而传统本体感觉只能提供有限的地面接触信息。在本文中，我们为配备触觉吸盘脚的气动驱动软四足机器人提出了一种基于学习的控制框架，并在物理硬件上进行了实验验证。该控制策略通过分阶段的学习过程在仿真中进行训练，从参考步态开始，逐步在随机环境中优化。所得到的控制器将本体感觉和触觉反馈映射到协调的气动驱动和吸盘命令，使机器人能够在平坦和倾斜表面上实现闭环运动。当部署在真实机器人上时，闭环策略优于开环基线，使在平坦表面上的前进速度提高了41%，在5度倾斜表面上提高了91%。消融研究进一步证明了触觉力估计和惯性反馈在稳定运动中的作用，与没有感官反馈的配置相比，性能提高了最高56%。
英文摘要：
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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