Work

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

Year: 2025

Type: article

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.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3583630

Authors: Andrea Usai, Alessandro Rizzo

Institution: Politecnico di Torino

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: European Commission