Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments

This paper presents Volitional Ohm, a novel entropy-based meta-learning strategy designed to enhance the computational intelligence of robotic systems navigating dynamic, disrupted environments. When a robotic agent encounters a catastrophic environmental shift—termed an Om event—its predictive model fails, triggering a high-entropy state of chaotic exploration. The Volitional Ohm mechanism, a meta-learning rule, enables the agent to dynamically modulate internal constraints (e.g., exploration rate) to guide this chaotic phase (Ohm) and rapidly converge to a stable, low-entropy policy (Omega). We validate this approach in a simulated robotic navigation task using a modified FrozenLake environment. Across 50 trials, the Volitional Agent converged significantly faster (M = 65.48 episodes) than fixed-constraint (M = 110.32) and random-constraint agents (p < 0.001, Cohen’s d = 3.18). Mutual information and sample entropy analyses confirm superior policy coherence and efficient chaos-to-order transitions. This framework, compatible with deep reinforcement learning, offers a practical, falsifiable method for optimizing robotic intelligence in unpredictable settings, contributing to the advancement of adaptive intelligent systems.