Yue Wen

Title: Yue Wen and Reinforcement Learning for Powered Prostheses.

Introduction

Yue Wen is an inventor associated with Raleigh, North Carolina. His work includes a patent describing how reinforcement learning can help tune the control of a powered prosthesis to support a target gait characteristic.

Latest Patents

Wen is associated with 1 patent: “Systems and Methods for Reinforcement Learning Control of a Powered Prosthesis.” The invention describes a powered prosthesis with a joint, motor, sensors, a finite state machine, and an impedance controller. Sensors measure gait parameters, while the finite state machine identifies the gait-cycle state. The impedance controller adjusts motor torque according to the measured parameters and state-dependent impedance settings. A reinforcement learning controller uses training data to tune those settings toward a target gait characteristic.

Career Highlights

The supplied information lists North Carolina State University and Arizona State University among the organizations where Wen worked. His patent reflects an application of reinforcement learning to prosthetic control, combining gait sensing, state identification, and motor adjustment.

Collaborations

Wen’s listed coworkers include He Huang and Jennie Si. Their names are provided as collaborators, though specific roles in the patent or related work are not specified.

Conclusion

Yue Wen’s patent describes a data-driven approach to tuning powered prostheses, with the goal of adapting motor control to gait-cycle conditions and achieving a target gait characteristic.