2025-11-20 ノースカロライナ州立大学(NC State)
<関連情報>
- https://news.ncsu.edu/2025/11/modified-prosthetics-help-amputees/
- https://ieeexplore.ieee.org/document/11251175
- https://ieeexplore.ieee.org/document/8613842
ロボット膝関節制御の二段階最適化による人間とロボットの共生への取り組み Addressing Human-Robot Symbiosis via Bilevel Optimization of Robotic Knee Prosthesis Control
Wentao Liu; Varun Nalam; Jennie Si; He Huang
IEEE Transactions on Robotics Published: 18 November 2025
DOI:https://doi.org/10.1109/TRO.2025.3634368
Abstract
This study presents an innovative solution for integrating a human and a robotic knee prosthesis symbiotically for walking. Achieving this requires human-robot cross-joint coordination to provide personalized walking assistance. Our approach uses inverse reinforcement learning (IRL) to identify control objectives for reinforcement learning (RL) controller. Unlike existing methods that optimize performance of human or robot alone, our approach considers both human (thigh segmental angle) and robot (knee joint kinematics) aspects. This bilevel optimization method was evaluated on 3 non-disabled participants and 2 people with amputation. Results showed that the approach personalized the objective function and resulted in a robust policy, completing optimization within a duration of 3.5 minutes. Compared to previous approaches focusing only on robot states, this symbiotic approach increased stance time and step length on the prosthesis side for most participants. Our results highlight the potential of integrating human state into prosthesis control personalization, enhancing the functionality and health of people with amputation.
ロボット膝関節のパーソナライゼーションのためのオンライン強化学習制御 Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis
Yue Wen; Jennie Si; Andrea Brandt; Xiang Gao; He Helen Huang
IEEE Transactions on Cybernetics Published:16 January 2019
DOI:https://doi.org/10.1109/TCYB.2019.2890974
Abstract
Robotic prostheses deliver greater function than passive prostheses, but we face the challenge of tuning a large number of control parameters in order to personalize the device for individual amputee users. This problem is not easily solved by traditional control designs or the latest robotic technology. Reinforcement learning (RL) is naturally appealing. The recent, unprecedented success of AlphaZero demonstrated RL as a feasible, large-scale problem solver. However, the prosthesis-tuning problem is associated with several unaddressed issues such as that it does not have a known and stable model, the continuous states and controls of the problem may result in a curse of dimensionality, and the human-prosthesis system is constantly subject to measurement noise, environmental change and human-body-caused variations. In this paper, we demonstrated the feasibility of direct heuristic dynamic programming, an approximate dynamic programming (ADP) approach, to automatically tune the 12 robotic knee prosthesis parameters to meet individual human users’ needs. We tested the ADP-tuner on two subjects (one able-bodied subject and one amputee subject) walking at a fixed speed on a treadmill. The ADP-tuner learned to reach target gait kinematics in an average of 300 gait cycles or 10 min of walking. We observed improved ADP tuning performance when we transferred a previously learned ADP controller to a new learning session with the same subject. To the best of our knowledge, our approach to personalize robotic prostheses is the first implementation of online ADP learning control to a clinical problem involving human subjects.
