A Neuro-Inspired Control Architecture to Enhance Robot Self-Preservation and Adaptation in Autonomous Navigation Tasks

Abstract

Ensuring survival and self-preservation is essential to design intelligent robots that adapt to dynamic and unfamiliar environments. Inspired by the dual-pathway model from neuroscience, we introduce a control architecture designed to ensure the adaptability of robotic behavior during navigation. This approach parallels the neuroscientific “Low Road” paradigm by incorporating constructs resembling the thalamus, implemented as a nonlinear filter; the amygdala, modeled as a Soft Actor-Critic (SAC) reinforcement learning agent; and the brainstem-cerebellum connection, represented by a Nonlinear Model Predictive Controller (NMPC). Our findings indicate superior adaptiveness, generalizability, and computational efficiency compared to standard NMPCs and Artificial Potential Fields in both static and dynamic environments with obstacles of varying risk levels.