Journal
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Volume -, Issue -, Pages 4066-4071Publisher
IEEE
DOI: 10.1109/iros40897.2019.8968464
Keywords
Assist-as-needed controller; robot-assisted training; reinforcement learning; wearable robotics; rehabilitation robotics
Categories
Funding
- New Jersey Health Foundation [PC 6-18]
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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.
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