Nowadays industrial robots have become the key equipment in the context of smart
manufacturing and the assembly process is seen as one of the dominant fields of robotic …

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 …

Over the past few years, there has been a noticeable surge in efforts to design novel tools
and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with …

This is the first attempt at considering human influence in the reinforcement learning control
of a robotic lower limb prosthesis toward symmetrical walking in real world situations. We …

We aim at creating a transfer reinforcement learning framework that allows the design of
learning controllers to leverage prior knowledge extracted from previously learned tasks and …

In this work, time-driven learning refers to the machine learning method that updates
parameters in a prediction model continuously as new data arrives. Among existing …

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) …

Assist-as-needed (AAN) control aims at promoting therapeutic outcomes in robot-assisted
rehabilitation by encouraging patients' active participation. Impedance control is used by …

Goal: Accounting for gait individuality is important to positive outcomes with wearable robots,
but manually tuning multi-activity models is time-consuming and not viable in a clinic …

We developed a novel framework for deep reinforcement learning (DRL) algorithms in task
constrained path generation problems of robotic manipulators leveraging human …