Semantic Scene Understanding for Human-Robot Interaction

KTH, Robotik, perception och lärande, RPL Georgia Institute of Technology, Atlanta, Georgia, USA J.P.Morgan AI Research, New York, NY, USA Vrije Universiteit (VU) Amsterdam, Amsterdam, NETHERLANDS Georgia Institute of Technology, Atlanta, Georgia, USA Association for Computing Machinery (ACM) 2023

Service robots will be co-located with human users in an unstructured human-centered environment and will benefit from understanding the user's daily activities, preferences, and needs towards fully assisting them. This workshop aims to explore how abstract semantic knowledge of the user's environment can be used as a context in understanding and grounding information regarding the user's instructions, preferences, habits, and needs. While object semantics have primarily been investigated for robotics in the perception and manipulation domain, recent works have shown the benefits of semantic modeling in a Human-Robot Interaction (HRI) context toward understanding and assisting human users. This workshop focuses on semantic information that can be useful in generalizing and interpreting user instructions, modeling user activities, anticipating user needs, and making the internal reasoning processes of a robot more interpretable to a user. Therefore, the workshop builds on topics from prior workshops such as Learning in HRI1, behavior adaptation for assistance2, and learning from humans3 and aims at facilitating cross-pollination across these domains through a common thread of utilizing abstract semantics of the physical world towards robot autonomy in assistive applications. We envision the workshop to touch on research areas such as unobtrusive learning from observations, preference learning, continual learning, enhancing the transparency of autonomous robot behavior, and user adaptation. The workshop aims to gather researchers working on these areas and provide fruitful discussions towards autonomous assistive robots that can learn and ground scene semantics for enhancing HRI.
Part of ISBN 9781450399708QC 20230801

human-centered autonomy; robot learning; scene semantics; Robotics; Robotteknik och automation; Human Computer Interaction; Människa-datorinteraktion (interaktionsdesign); Conference paper; info:eu-repo/semantics/conferenceObject; text

10.1145.3568294.3579960

Open access content. Open access content
info:eu-repo/semantics/restrictedAccess

English

UPE oai:DiVA.org:kth-333369
0000-0002-1733-7019
doi:10.1145/3568294.3579960
ISI:001054975700211
Scopus 2-s2.0-85150420639
1400071460

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