Robot Evolution

Robot evolution uses evolutionary algorithms to optimize both the physical design (morphology) and control systems (brains) of robots, aiming to create more adaptable and efficient machines. Current research focuses on improving the efficiency of evolutionary processes, particularly through techniques like continuous evolution and the incorporation of Lamarckian inheritance (where learned traits are passed to offspring), often coupled with model-based reinforcement learning for more sample-efficient training. These advancements are significant because they enable the automated design of robots capable of complex tasks, potentially reducing development costs and leading to more robust and versatile robotic systems for various applications.