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

Snake Robot with Tactile Perception Navigates on Large-scale Challenging Terrain