  Along with the advancement of robot skin technology, there has been notable
progress in the development of snake robots featuring body-surface tactile
perception. In this study, we proposed a locomotion control framework for snake
robots that integrates tactile perception to augment their adaptability to
various terrains. Our approach embraces a hierarchical reinforcement learning
(HRL) architecture, wherein the high-level orchestrates global navigation
strategies while the low-level uses curriculum learning for local navigation
maneuvers. Due to the significant computational demands of collision detection
in whole-body tactile sensing, the efficiency of the simulator is severely
compromised. Thus a distributed training pattern to mitigate the efficiency
reduction was adopted. We evaluated the navigation performance of the snake
robot in complex large-scale cave exploration with challenging terrains to
exhibit improvements in motion efficiency, evidencing the efficacy of tactile
perception in terrain-adaptive locomotion of snake robots.
