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Learning to generate pointing gestures in situated embodied conversational agents
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-3135-5683
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-7801-7617
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-1399-6604
2023 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 10, article id 1110534Article in journal (Refereed) Published
Abstract [en]

One of the main goals of robotics and intelligent agent research is to enable them to communicate with humans in physically situated settings. Human communication consists of both verbal and non-verbal modes. Recent studies in enabling communication for intelligent agents have focused on verbal modes, i.e., language and speech. However, in a situated setting the non-verbal mode is crucial for an agent to adapt flexible communication strategies. In this work, we focus on learning to generate non-verbal communicative expressions in situated embodied interactive agents. Specifically, we show that an agent can learn pointing gestures in a physically simulated environment through a combination of imitation and reinforcement learning that achieves high motion naturalness and high referential accuracy. We compared our proposed system against several baselines in both subjective and objective evaluations. The subjective evaluation is done in a virtual reality setting where an embodied referential game is played between the user and the agent in a shared 3D space, a setup that fully assesses the communicative capabilities of the generated gestures. The evaluations show that our model achieves a higher level of referential accuracy and motion naturalness compared to a state-of-the-art supervised learning motion synthesis model, showing the promise of our proposed system that combines imitation and reinforcement learning for generating communicative gestures. Additionally, our system is robust in a physically-simulated environment thus has the potential of being applied to robots.

Place, publisher, year, edition, pages
Frontiers Media SA , 2023. Vol. 10, article id 1110534
Keywords [en]
reinforcement learning, imitation learning, non-verbal communication, embodied interactive agents, gesture generation, physics-aware machine learning
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-326625DOI: 10.3389/frobt.2023.1110534ISI: 000970385800001PubMedID: 37064574Scopus ID: 2-s2.0-85153351800OAI: oai:DiVA.org:kth-326625DiVA, id: diva2:1755473
Note

QC 20230508

Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-05-08Bibliographically approved

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Deichler, AnnaWang, SiyangAlexanderson, SimonBeskow, Jonas

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