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Online inference of human belief for cooperative robots

Bühler, Moritz and Weisswange, Thomas (2018):
Online inference of human belief for cooperative robots.
In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October, 1-5, 2018, DOI: 10.1109/IROS.2018.8594076, [Online-Edition: http://tuprints.ulb.tu-darmstadt.de/8295/2/IROS2018_AV.pdf],
[Conference or Workshop Item]

Abstract

For human-robot cooperation, inferring a human's cognitive state is very important for an efficient and natural interaction. Similar to human-human cooperation, understanding what the partner plans and knowing, if he is situation aware, is necessary to prevent collisions, offer support at the right time, correct mistakes before they happen or choose the best actions for oneself as early as possible. We propose a model-based belief filter to extract relevant aspects of a human's mental state online during cooperation. It performs inference based on human actions and its own task knowledge, modeling cognitive processes like perception and action selection. In contrast to most prior work, we explicitly estimate the human belief instead of inferring only a single mode or intention. Since this is a double inference process, we focus on representing the human estimates of environmental state and task as well as corresponding uncertainties. We designed a human-robot cooperation experiment that allowed for a variety of cognitive states of both agents and collected data to test and evaluate the proposed belief filter. The results are promising, as our system can be used to provide reasonable predictions of the human action and insights into his situation awareness. At the same time it is inferring interpretable information about the underlying cognitive states - A belief about the human's belief about the environment.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Bühler, Moritz and Weisswange, Thomas
Title: Online inference of human belief for cooperative robots
Language: English
Abstract:

For human-robot cooperation, inferring a human's cognitive state is very important for an efficient and natural interaction. Similar to human-human cooperation, understanding what the partner plans and knowing, if he is situation aware, is necessary to prevent collisions, offer support at the right time, correct mistakes before they happen or choose the best actions for oneself as early as possible. We propose a model-based belief filter to extract relevant aspects of a human's mental state online during cooperation. It performs inference based on human actions and its own task knowledge, modeling cognitive processes like perception and action selection. In contrast to most prior work, we explicitly estimate the human belief instead of inferring only a single mode or intention. Since this is a double inference process, we focus on representing the human estimates of environmental state and task as well as corresponding uncertainties. We designed a human-robot cooperation experiment that allowed for a variety of cognitive states of both agents and collected data to test and evaluate the proposed belief filter. The results are promising, as our system can be used to provide reasonable predictions of the human action and insights into his situation awareness. At the same time it is inferring interpretable information about the underlying cognitive states - A belief about the human's belief about the environment.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Event Location: Madrid, Spain
Event Dates: October, 1-5, 2018
Date Deposited: 27 Jan 2019 20:55
DOI: 10.1109/IROS.2018.8594076
Official URL: http://tuprints.ulb.tu-darmstadt.de/8295/2/IROS2018_AV.pdf
URN: urn:nbn:de:tuda-tuprints-82953
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