Knowledge-guided robot learning on compliance control for robotic assembly task with predictive model
Q Liu, Z Ji, W Xu, Z Liu, B Yao, Z Zhou - Expert Systems with Applications, 2023 - Elsevier
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 …
manufacturing and the assembly process is seen as one of the dominant fields of robotic …
Toward expedited impedance tuning of a robotic prosthesis for personalized gait assistance by reinforcement learning control
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 …
initial feasibility of automating the process of knee prosthesis control parameter tuning has …
AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review
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 …
and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with …
Human-robotic prosthesis as collaborating agents for symmetrical walking
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 …
of a robotic lower limb prosthesis toward symmetrical walking in real world situations. We …
Reinforcement learning control with knowledge shaping
X Gao, J Si, H Huang - IEEE Transactions on Neural Networks …, 2023 - ieeexplore.ieee.org
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 …
learning controllers to leverage prior knowledge extracted from previously learned tasks and …
Online reinforcement learning control by direct heuristic dynamic programming: From time-driven to event-driven
Q Zhao, J Si, J Sun - IEEE transactions on neural networks and …, 2021 - ieeexplore.ieee.org
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 …
parameters in a prediction model continuously as new data arrives. Among existing …
User controlled interface for tuning robotic knee prosthesis
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) …
for users and clinicians. An automatic tuning approach using reinforcement learning (RL) …
Shaping individualized impedance landscapes for gait training via reinforcement learning
Assist-as-needed (AAN) control aims at promoting therapeutic outcomes in robot-assisted
rehabilitation by encouraging patients' active participation. Impedance control is used by …
rehabilitation by encouraging patients' active participation. Impedance control is used by …
Predicting individualized joint kinematics over continuous variations of walking, running, and stair climbing
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 …
but manually tuning multi-activity models is time-consuming and not viable in a clinic …
Koopman operator–based knowledge-guided reinforcement learning for safe human–robot interaction
We developed a novel framework for deep reinforcement learning (DRL) algorithms in task
constrained path generation problems of robotic manipulators leveraging human …
constrained path generation problems of robotic manipulators leveraging human …