Artificial fear for the control of autonomous robots

We present a neuro-inspired control framework for autonomous robots that integrates an artificial emotion of fear, drawing inspiration from LeDoux's dual-pathway hypothesis. To replicate the "Low Road" pathway, our system comprises proxies for the thalamus, implemented as a nonlinear filter; the amygdala, modeled as a Soft Actor-Critic (SAC) reinforcement learning agent; the brainstem, orchestrated through a Nonlinear Model Predictive Controller (NMPC). The NMPC's parameters are adjusted by the amygdala, enabling it to generate control inputs to actuate the robot. Our preliminary results demonstrate that the robot exhibits a better adaptive behavior than a standard NMPC in both static and dynamic environments with obstacles characterized by different hazard levels.