84 citations · 96 across the 6 of their papers we have counts for
7 papers
Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment
Ruofan Wu, Minhan Li, Zhikai Yao +3
Automatically configuring a robotic prosthesis to fit its user's needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Pre…
A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton
Xikai Tu, Minhan Li, Ming Liu +3
Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human move…
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
Xiang Gao, Jennie Si, Yue Wen +3
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability…
Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control
Minhan Li, Yue Wen, Xiang Gao +2
Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning…
Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation
Frank E. Curtis, Minhan Li
Gradient sampling (GS) has proved to be an effective methodology for the minimization of objective functions that may be nonconvex and/or nonsmooth. The most computationally expens…
Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers
Hiva Ghanbari, Minhan Li, Katya Scheinberg
The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Mi…
Active Metric Learning for Supervised Classification
Krishnan Kumaran, Dimitri Papageorgiou, Yutong Chang +2
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find…