Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse

Automatically personalizing complex control of robotic prostheses to improve gait performance, such as gait symmetry, is challenging. Recently, human-in-the-loop (HIL) optimization and reinforcement learning (RL) have shown promise in achieving optimized control for each individual user in the field of wearable robots. However, HIL optimization methods lack scalability for high-dimensional space, while RL has mostly focused on optimizing robot kinematic performance. Thus, we propose a novel hierarchical framework to personalize robotic knee prosthesis control and improve overall gait performance. Specifically, in this study the framework was implemented to simultaneously design target knee kinematics and tune 12 impedance control parameters for improved symmetry of propulsive impulse in walking. In our proposed framework, HIL optimization is used to identify an optimal target knee kinematics with respect to symmetry improvement, while RL is leveraged to yield an optimal policy for tuning impedance parameters in high-dimensional space to match the kinematics target. The proposed framework was validated on human subjects, walking with our robotic knee prosthesis. The results showed that our design successfully shaped the target knee kinematics as well as configured 12 impedance control parameters to improve propulsive impulse symmetry of the human users. The knee kinematics that yielded best propulsion symmetry did not preserve the normative knee kinematics profile observed in non-disabled individuals, suggesting that restoration of normative joint biomechanics in walking does not necessarily optimize the gait performance of human-prosthesis systems. This new framework for prosthesis control personalization may be extended to other wearable devices or different gait performance optimization goals in the future.