Name
Abstract | ||
|---|---|---|
In robot-assisted rehabilitation, assist-as-needed (AAN) controllers have been proposed to promote subjects' active participation, which is thought to lead to better training outcomes. Most of these AAN controllers require a patient-specific manual tuning of the parameters defining the underlying force-field, which typically results in a tedious and time-consuming process. In this paper, we propose a reinforcement-learning-based impedance controller that actively reshapes the stiffness of the force-field to the subject's performance, while providing assistance only when needed. This adaptability is made possible by correlating the subject's most recent performance to the ultimate control objective in real-time. In addition, the proposed controller is built upon action dependent heuristic dynamic programming using the actor-critic structure, and therefore does not require prior knowledge of the system model. The controller is experimentally validated with healthy subjects through a simulated ankle mobilization training session using a powered ankle-foot orthosis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968464 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Keywords | Field | DocType |
Assist-as-needed controller, robot-assisted training, reinforcement learning, wearable robotics, rehabilitation robotics | Wearable robot,Adaptability,Control theory,Computer science,Control engineering,Rehabilitation robotics,Heuristic dynamic programming,System model,Reinforcement learning | Conference |
ISSN | Citations | PageRank |
2153-0858 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufeng Zhang | 1 | 117 | 30.05 |
| Shuai Li | 2 | 0 | 2.37 |
| Karen J. Nolan | 3 | 1 | 3.81 |
| Damiano Zanotto | 4 | 55 | 12.02 |