  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.
