Bayesian Deep Predictive Coding for Snake-like Robotic Control in Unknown Terrains

Abstract

Effectively modeling the spatio-temporal interactions both internally and externally is a challenge in controlling multi-linked snake robots. This paper presents an effective method based on deep predictive coding: SnakeFormer, to address the aforementioned issue. The main contributions include: 1) Deriving a variational free energy function with two innovative regularization terms through Bayesian probabilistic analysis, offering a novel perspective to simulate the interactions between agent and the environment; 2) Introducing an interaction-attention model within a Transformer structure for predicting dynamics, and collaboratively addressing path planning and obstacle avoidance tasks. 3) By incorporating serpenoid embedding and optimizing self-attention computations, the gait stability and motion efficiency are improved. Preliminary experiments and comparative analysis with baseline models fully validate the effectiveness and generalizability of the method.