Affiliation:
1. The University of Texas at Austin
Abstract
In shared control, a robot and a human works together to accomplish a task in order to increase the efficiency of tele-operation. To assist the user in shared control, the robot has to predict the intent of the user accurately and quickly, but this may not be possible when the user can not adapt to the delay as the prediction of the robot depends of the inputs of the user. In this work, we propose an algorithm for intent prediction in shared control while providing performance guarantees when the user has a feedback delay. We assess the feasibility of our algorithm on a case study.
Subject
General Medicine,General Chemistry
Cited by
2 articles.
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1. Deep Learning-based human activity recognition using RGB images in Human-robot collaboration;Proceedings of the Human Factors and Ergonomics Society Annual Meeting;2022-09
2. Novel Developments in Formal Methods for Human Factors Engineering;Proceedings of the Human Factors and Ergonomics Society Annual Meeting;2017-09