Toward Task-Independent Optimal Adaptive Control of a Hip Exoskeleton for Locomotion Assistance in Neurorehabilitation

Personalized robotic exoskeleton control is essential in assisting individuals with motor deficits. However, current research still lacks a solution from the end of a practical need of the problem to the end of its successful demonstration in physical environments, namely an end-to-end solution, that enables stable and continuous walking across different tasks. This study addresses this challenge by introducing a hierarchical control framework for the purpose. At the low level, impedance control ensures joint compliance without causing injury to users. At the high level, a reinforcement learning (RL)-based optimal adaptive controller automatically personalizes assistance to both hip extension and flexion (namely, bi-directional) to reach a target range of motion (ROM) under multiple walking conditions. As the first potentially feasible approach to this challenging problem and to meet practical use requirements, we developed a least-square policy iteration-based solution to configure the intrinsic parameters within the well-established finite state machine impedance control (FSM-IC). We successfully tested the control solution on eight young unimpaired participants and one participant post-stroke wearing a hip exoskeleton while walking on an instrumented treadmill. The proposed method can be applied to solving for optimal impedance parameters for individual users and different task scenarios to increase joint ROM. Our next step is to further evaluate this solution framework on additional people with hemiparesis who may benefit from hip joint assistance in therapy or daily activities to restore normative or improve gait patterns.