Name
Abstract | ||
|---|---|---|
The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BioRob49111.2020.9224392 | BioRob |
Keywords | DocType | ISSN |
Assist-as-needed controller, robot-assisted gait training, reinforcement learning, wearable robotics, rehabilitation robotics | Conference | 2155-1782 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufeng Zhang | 1 | 117 | 30.05 |
| Shuai Li | 2 | 0 | 0.34 |
| Karen J. Nolan | 3 | 1 | 3.81 |
| Damiano Zanotto | 4 | 55 | 12.02 |