让机器人像人脑一样实时自适应，无需重训即可应对突发环境变化。
Why Evolve When You Can Adapt? Post-Evolution Adaptation of Genetic Memory for On-the-Fly Control
- 用遗传算法生成控制器，再结合突触可塑性动态调整权重。
- 在光照和障碍物变化时，导航成功率提升至92%以上。
- 适合需要快速应变的智能机器人场景，如救援或巡检。
设想一种机器人控制器，能像人类突触一样自适应，实时重连以应对未知挑战。本文提出一种新颖的零样本自适应机制，将标准遗传算法（GA）控制器与在线海布型可塑性结合。受生物系统启发，该方法区分学习与记忆：基因型作为记忆，海布更新负责学习。在本方法中，适应度函数被用作海布学习的实时缩放因子，使机器人神经控制器能在运行时动态调整突触权重，无需额外训练。这一动态适应层仅在运行时激活，用于应对突发环境变化。任务结束后，机器人‘遗忘’临时调整，恢复原始权重，保留核心知识。我们在e-puck机器人上验证了该混合GA-海布控制器在改变光照条件和障碍物的T型迷宫导航任务中的表现。
原文摘要 · Abstract (English)
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.
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