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

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 zeroshot adaptation mechanism for evolutionary robotics, merging a standard Genetic Algorithm (GA) controller with online Hebbian plasticity, drawing inspiration from biological systems through the separation of learning and memory, with the genotype acting as memory for the robot and the learning through Hebbian updates. In the proposed approach, the fitness function itself is leveraged as a live scaling factor for Hebbian learning, so that the robot’s neural controller can adjust its synaptic weights on-the-fly with no additional training. This adds a dynamic adaptive layer that activates only during runtime to handle unexpected changes to the environment. After the task, the robot ”forgets” the temporary adjustments and reverts to the original weights, preserving core knowledge. An e-puck robot is used to validate the hybrid GA–Hebbian controller within a T-maze navigation task with changing light conditions and obstacles.