Co Adaptation

Co-adaptation research focuses on the simultaneous optimization of interacting systems, such as a robot's morphology and control algorithms, or a human and a robotic rehabilitation device. Current efforts utilize reinforcement learning, often incorporating graph neural networks or co-kriging adjustments to improve efficiency and address challenges like premature convergence in optimization and the simulation-to-reality gap. This interdisciplinary field is significant for advancing robotics, human-computer interaction, and federated learning by enabling more efficient and adaptable systems, particularly in resource-constrained environments.