Why Evolve When You Can Adapt? Post-Evolution Adaptation of Genetic Memory for On-the-Fly Control

2025-08-05

AI Summary

To address the challenge of online adaptation for robot controllers under unforeseen environmental perturbations—without compromising pre-learned knowledge—this paper proposes a zero-shot runtime adaptation mechanism. Methodologically, it decouples memory (fixed weights pre-optimized via genetic algorithms) from online learning (Hebbian synaptic plasticity), dynamically scaling plasticity strength via task fitness as a modulatory factor; upon task completion, parameters automatically revert to their initial state—enabling “learn-and-forget” behavior. Crucially, the mechanism requires no auxiliary training, backpropagation, or gradient-based updates, ensuring both biological plausibility and engineering practicality. Evaluated on an e-puck robot performing T-maze navigation, the system maintains robust trajectory tracking amid abrupt illumination changes and sudden obstacle appearances. Results demonstrate strong robustness, instantaneous adaptability, and strict preservation of original controller knowledge.

Technology Category

Intelligent Robots: Behavior Learning & ControlCognitive Modeling & Cognitive Systems: Adaptive BehaviorMachine Learning: Adversarial Learning & Robustness

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Social networks and social learningUser Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Imagine a robot controller with the ability to adapt like human synapses, dynamically rewiring itself to overcome unforeseen challenges in real time. This paper proposes a novel zero-shot adaptation mechanism for evolutionary robotics, merging a standard Genetic Algorithm (GA) controller with online Hebbian plasticity. Inspired by biological systems, the method separates learning and memory, with the genotype acting as memory and Hebbian updates handling learning. In our approach, the fitness function is leveraged as a live scaling factor for Hebbian learning, enabling the robot's neural controller to adjust synaptic weights on-the-fly without additional training. This adds a dynamic adaptive layer that activates only during runtime to handle unexpected environmental changes. After the task, the robot 'forgets' the temporary adjustments and reverts to the original weights, preserving core knowledge. We validate this hybrid GA-Hebbian controller on an e-puck robot in a T-maze navigation task with changing light conditions and obstacles.

Problem

• Develops zero-shot adaptation for evolutionary robotics controllers
• Combines Genetic Algorithm with online Hebbian plasticity
• Enables real-time neural weight adjustment without retraining

Innovation

• Hybrid GA-Hebbian controller for real-time adaptation
• Fitness function scales Hebbian learning dynamically
• Post-task memory reversion preserves core knowledge