# Hierarchical RL-Guided Large-scale Navigation of a Snake Robot

arXiv: https://arxiv.org/abs/2312.03223 · HTML: https://mlsanity.com/paper/2312.03223/

## 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)

Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this work, we present a four-layer hierarchical control scheme to enable the snake robot to navigate freely in large-scale environments. The proposed model decomposes navigation into global planning, local planning, gait generation, and gait tracking. Using reinforcement learning (RL) and a central pattern generator (CPG), our method learns to navigate in complex mazes within hours and can be directly deployed to arbitrary new environments in a zero-shot fashion. We use the high-fidelity model of Northeastern's slithering robot COBRA to test the effectiveness of the proposed hierarchical control approach.