  This study presents an innovative approach to optimal gait control for a soft
quadruped robot enabled by four Compressible Tendon-driven Soft Actuators
(CTSAs). Improving our previous studies of using model-free reinforcement
learning for gait control, we employ model-based reinforcement learning (MBRL)
to further enhance the performance of the gait controller. Compared to rigid
robots, the proposed soft quadruped robot has better safety, less weight, and a
simpler mechanism for fabrication and control. However, the primary challenge
lies in developing sophisticated control algorithms to attain optimal gait
control for fast and stable locomotion. The research employs a multi-stage
methodology, including state space restriction, data-driven model training, and
reinforcement learning algorithm development. Compared to benchmark methods,
the proposed MBRL algorithm, combined with post-training, 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.
