Optimal Gait Control for a Tendon-Driven Soft Quadruped Robot by Model-Based Reinforcement Learning

This study presents an innovative approach to optimal gait control for a soft quadruped robot enabled by four compressible tendon-driven soft actuators. Soft quadruped robots, compared to their rigid counterparts, are widely recognized for offering enhanced safety, lower weight, and simpler fabrication and control mechanisms. However, their highly deformable structure introduces nonlinear dynamics, making precise gait locomotion control complex. To solve this problem, we propose a novel model-based reinforcement learning (MBRL) method. The study employs a multi-stage approach, including state space restriction, data-driven surrogate model training, and MBRL development. Compared to benchmark methods, the proposed approach significantly improves the efficiency and performance of gait control policies. The developed policy is both robust and adaptable to the robot's deformable morphology. The study concludes by highlighting the practical applicability of these findings in real-world scenarios.