SENSE-STEP: Learning Sim-to-Real Locomotion for a Sensory-Enabled Soft Quadruped Robot

TL;DR

This paper introduces a learning-based control framework for a soft quadruped robot with tactile sensors, enabling robust closed-loop locomotion in various environments through simulation training and sensory feedback integration.

Contribution

It presents a novel staged learning process for sim-to-real transfer of control policies that incorporate tactile and proprioceptive feedback for soft robots.

Findings

41% increase in forward speed on flat surfaces

91% increase in speed on inclined surfaces

Sensory feedback significantly improves stability and performance

Abstract

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.