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Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Trick, Susanne ; Koert, Dorothea ; Peters, Jan ; Rothkopf, Constantin A. (2022):
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. (Postprint)
In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7009-7016,
Darmstadt, IEEE, International Conference on Intelligent Robots and Systems (IROS), Macau, China, 03.-08.11.2019, e-ISSN 2153-0866, ISBN 978-1-7281-4004-9,
DOI: 10.26083/tuprints-00020552,
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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Postprint
Title: Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction
Language: English
Abstract:

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalities for intention recognition may not just increase the robustness against failure of individual modalities but especially reduce the uncertainty about the intention to be recognized. This is desirable as particularly in direct interaction between robots and potentially vulnerable humans a minimal uncertainty about the situation as well as knowledge about this actual uncertainty is necessary. Thus, in contrast to existing methods, in this work a new approach for multimodal intention recognition is introduced that focuses on uncertainty reduction through classifier fusion. For the four considered modalities speech, gestures, gaze directions and scene objects individual intention classifiers are trained, all of which output a probability distribution over all possible intentions. By combining these output distributions using the Bayesian method Independent Opinion Pool [1] the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative human-robot interaction task with a 7-DoF robot arm. The results show that fused classifiers, which combine multiple modalities, outperform the respective individual base classifiers with respect to increased accuracy, robustness, and reduced uncertainty.

Book Title: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 Seiten
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Divisions: 20 Department of Computer Science > Intelligent Autonomous Systems
TU-Projects: EC/H2020|640554|SKILLS4ROBOTS
Event Title: International Conference on Intelligent Robots and Systems (IROS)
Event Location: Macau, China
Event Dates: 03.-08.11.2019
Date Deposited: 18 Nov 2022 14:15
Last Modified: 18 Nov 2022 14:15
DOI: 10.26083/tuprints-00020552
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-205520
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20552
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