AI-computing, deep reinforcement learning-based predictive human-robot neuromechanical simulation for wearable robots
摘要
Human-robot interaction (HRI) is widely used in robotics to assist humans, with wearable robots enhancing mobility for both able-bodied individuals and those with impairments. Traditionally, characterizing human biomechanical responses to these robots requires extensive human testing, which is time-consuming, costly, and potentially risky. Developing computational HRI simulations for wearable robots offers a promising solution. However, modeling the high-fidelity human-exoskeleton interaction in simulations presents significant challenges that remain underexplored. These include creating a high-fidelity autonomous human motion control agent, accounting for the non-passive nature of human responses, and incorporating closed-loop control within the robotic system. In this paper, we propose an AI-computing, deep reinforcement learning-based HRI simulation to predict complex and realistic human biomechanical responses to exoskeleton assistance. The multi-neural network training process develops an end-to-end, autonomous control policy that reduces human muscle effort by utilizing current human kinematic states. This approach processes state information from both the human musculoskeletal and exoskeleton control neural network, generating control policies for robust human walking movement and reducing muscle effort. Numerical experiments demonstrated the framework’s ability to simulate human motion control, showing reductions in hip joint torque (13.04