A Neuro-Inspired Control Architecture to Enhance Robot Self-Preservation and Adaptation in Autonomous Navigation Tasks
Andrea Usai, Alessandro Rizzo
AI summary
Problem
Standard model-based and learning-based navigation methods struggle with manual weight tuning, poor generalization, and inadequate self-preservation in complex, dynamic environments.
Approach
Inspired by the neuroscience Low Road fear pathway, the system uses a reinforcement learning agent to assess environmental danger and dynamically adjust the weights of a nonlinear model predictive controller for adaptive navigation.
Key results
- Superior obstacle clearance and safety in static and dynamic scenarios
- Enhanced adaptability and generalizability across varying risk levels
- Improved computational efficiency compared to standard NMPC and APF
- Novel real-time reinforcement learning mechanism for NMPC weight tuning
Why it matters
Enables safer, more autonomous robots for critical real-world applications like search-and-rescue and infrastructure inspection where self-preservation is paramount.
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 neuro- science, we introduce a control architecture designed to ensure the adaptability of robotic behavior during navigation. This ap- proach 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)reinforcementlearningagent;andthebrainstem-cerebellum connection, represented by a Nonlinear Model Predictive Con- troller (NMPC). Our findings indicate superior adaptiveness, gen- eralizability, and computational efficiency compared to standard NMPCs and Artificial Potential Fields in both static and dynamic environments with obstacles of varying risk levels.