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Enhancing Robot Perception with Real-World HRI
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-3432-6151
2024 (English)In: HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2024, p. 160-162Conference paper, Published paper (Refereed)
Abstract [en]

Robot perception often fails in uncontrolled environments due to unfamiliar object classes, different domains, or hardware issues. This poses significant challenges for human-robot interaction (HRI) outside of a lab or user study settings. My work focuses on two separate approaches: improving robot perception models and developing systems where users can directly correct robot errors. My research strives to improve HRI in real-world scenarios by reducing vision errors and empowering users to address them.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2024. p. 160-162
Keywords [en]
failures, multimodality, perception, real world interaction, robotics
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-344810DOI: 10.1145/3610978.3638363Scopus ID: 2-s2.0-85188112646OAI: oai:DiVA.org:kth-344810DiVA, id: diva2:1847616
Conference
19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024, Boulder, United States of America, Mar 11 2024 - Mar 15 2024
Note

QC 20240409

 Part of ISBN 9798400703232

Available from: 2024-03-28 Created: 2024-03-28 Last updated: 2024-04-09Bibliographically approved

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Wozniak, Maciej K.

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  • apa
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  • de-DE
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  • fi-FI
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  • nn-NB
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Output format
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