May 8, 2025
Date: Friday, May 9th 
Time: 12 – 1 PM(ish)
Location: SIG 224 
Zoom: Zoom Link
Abstract: Feedforward/model-based controls for assistive devices often fail in real-world settings, and current optimization-based controls suffer from the difficulty of measuring and noisiness of human costs. We propose to use game theory as a framework to analyze, predict and design the human-machine system energy cost landscape and devise adaptive control strategies that can dynamically shift the system equilibria to achieve various goals. In order to achieve this, humans need to adapt and optimize their strategies in response to the new energy landscape created by the machine interaction. Here we ask whether and how people adapt the strategies they previously developed through broad experience on various energy landscapes using a split-belt treadmill with varying belt speeds.
Bio: Zijie Jin is a 2nd year PhD student with Kim Ingraham in ECE, and his research interests include understanding human locomotion to design and control exoskeletons and prostheses for rehabilitation and human augmentation.