Robot Self-Assessment And Expression: A Learning Framework

Abstract

Future autonomous robots that operate in teams with humans should have capabilities that facilitate intuitive and/or implicit communication, for example in the form of emotional expressions. These emotional expressions should be presented clearly to the human to promote adequate understanding of robot behaviors and intent. In this paper, we present a Robot Self-Assessment and Expression framework derived from reinforcement theory of motivation and the current state-of-the-art in machine learning. The proposed framework theoretically describes how a robot could display emotional expressions depending on both predicted outcomes and actual outcomes of a task. The end goal for this framework design will be for the robot to obtain anticipatory guidance and performance feedback from a human instructor during a training task. Future research and areas for testing and validation of the framework are discussed.

Publication Date

1-1-2017

Publication Title

Proceedings of the Human Factors and Ergonomics Society

Volume

2017-October

Number of Pages

1188-1192

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/1541931213601780

Socpus ID

85042466826 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85042466826

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