7 accepted papers
Human-in-the-loop (HIL) optimization usually optimizes assistive torque of exoskeletons to minimize the human's energetic expenditure in walking, quantified by metabolic cost. This formulation can, however, result in altered gait pattern of the human joint from the natural pattern, which is undesire…
Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Recently, human-in-the-loop (HIL) optimiza
This study aims to demonstrate reinforcement learning tracking control for automatically configuring the impedance parameters of a robotic knee prosthesis. While our previous studies involving human subjects have focused on tuning the impedance control parameters to meet a fixed, subjectively prescr
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 data-driven solution framework based on reinforce…
The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the opti…
Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environments is an unsolved problem. Reinforcement learning (RL) is a naturally p…
This paper aims to develop an optimal controller that can automatically provide personalized control of robotic knee prosthesis in order to best support gait of individual prosthesis wearers. We introduced a new reinforcement learning (RL) controller for this purpose based on the promising ability o…