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

arXiv: https://arxiv.org/abs/2312.03225 · HTML: https://mlsanity.com/paper/2312.03225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning-Based Model Matching to Reduce the Sim-Real Gap in COBRA](https://mlsanity.com/paper/2406.13700/) (2024)

## Abstract (arXiv)

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.