Abstract:
Shared control and shared autonomy play an important role in assistive technologies, allowing the offloading of the cognitive burden required for control from the user to...Show MoreMetadata
Abstract:
Shared control and shared autonomy play an important role in assistive technologies, allowing the offloading of the cognitive burden required for control from the user to the intelligent robotic device. In this context, electrophysiological measures of error detection, directly measured from a person's brain activity as Error-related Potentials (ErrPs), can be exploited to provide passive adaptation of an external semi-autonomous system to the human. This concept was implemented in an online robot learning task, where user's evaluation of the robot's actions, in terms of detected ErrP, was exploited to update a reward function in a Reinforcement Learning (RL) framework. Results from both simulated and experimental studies show that the introduction of human evaluation in the robot learning loop allows for: (1) the acceleration of optimal policy learning in a target reaching task, (2) the introduction of a further degree of control in robot learning, namely identification of one among multiple targets, according to the user's will. Overall, presented results support the potential of human-robot co-adaptive and co-operative strategies to develop human-centered assistive technologies.
Date of Conference: 21-25 May 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2577-087X