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Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment

Autor: Wu, Ruofan, Li, Minhan, Yao, Zhikai, Si, Jennie, He, Huang

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. Previously, we have successfully developed reinforcement learning (RL) so

Externí odkaz: http://arxiv.org/abs/2101.03487

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A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton

Autor: Tu, Xikai, Li, Minhan, Liu, Ming, Si, Jennie, He, Huang

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 movement still is a major challenge. This paper aims at developing a novel

Externí odkaz: http://arxiv.org/abs/2011.06116

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Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration

Autor: Gao, Xiang, Si, Jennie, Wen, Yue, Li, Minhan, He, Huang

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 and optimality at systems level. Existing approximate/adaptive dynamic

Externí odkaz: http://arxiv.org/abs/2006.09008

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Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control

Autor: Li, Minhan, Wen, Yue, Gao, Xiang, Si, Jennie, Huang, He Helen

Publikováno v: IEEE Transactions on Robotics, 2021

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 has been demonstrated in a principled way, the next critical issue is

Externí odkaz: http://arxiv.org/abs/2006.06518

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Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation

Autor: Curtis, Frank E., Li, Minhan

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 expensive component of a contemporary GS method is the need to solve a conve

Externí odkaz: http://arxiv.org/abs/2005.07822

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Akademický článek

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Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers

Autor: Ghanbari, Hiva, Li, Minhan, Scheinberg, Katya

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). Minimizing the reciprocals of these measures are the goals of supervised

Externí odkaz: http://arxiv.org/abs/1903.00359

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Active Metric Learning for Supervised Classification

Autor: Kumaran, Krishnan, Papageorgiou, Dimitri, Chang, Yutong, Li, Minhan, Takáč, Martin

Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extens

Externí odkaz: http://arxiv.org/abs/1803.10647

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