kth.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Do you follow?: A fully automated system for adaptive robot presenters
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.ORCID-id: 0000-0003-0112-6732
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.ORCID-id: 0000-0002-8579-1790
Antal upphovsmän: 22023 (Engelska)Ingår i: HRI 2023: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, s. 102-111Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

An interesting application for social robots is to act as a presenter, for example as a museum guide. In this paper, we present a fully automated system architecture for building adaptive presentations for embodied agents. The presentation is generated from a knowledge graph, which is also used to track the grounding state of information, based on multimodal feedback from the user. We introduce a novel way to use large-scale language models (GPT-3 in our case) to lexicalise arbitrary knowledge graph triples, greatly simplifying the design of this aspect of the system. We also present an evaluation where 43 participants interacted with the system. The results show that users prefer the adaptive system and consider it more human-like and flexible than a static version of the same system, but only partial results are seen in their learning of the facts presented by the robot.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM) , 2023. s. 102-111
Nyckelord [en]
adaptation, behaviour tree, feedback, knowledge graph, learning, lexicalisation, multimodal
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:kth:diva-333378DOI: 10.1145/3568162.3576958Scopus ID: 2-s2.0-85150369153OAI: oai:DiVA.org:kth-333378DiVA, id: diva2:1785048
Konferens
18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023, Stockholm, Sweden, Mar 13 2023 - Mar 16 2023
Anmärkning

Part of ISBN 9781450399647

QC 20230801

Tillgänglig från: 2023-08-01 Skapad: 2023-08-01 Senast uppdaterad: 2023-08-01Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

Axelsson, AgnesSkantze, Gabriel

Sök vidare i DiVA

Av författaren/redaktören
Axelsson, AgnesSkantze, Gabriel
Av organisationen
Tal, musik och hörsel, TMH
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 95 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf