Human-Robot/Agent Interaction is well researched in many areas, but approaches commonly either focus on dyadic interactions or crowd simulations. However, the intermediate structure between individuals and crowds, i.e., small groups, has been studied less. In small group situations, it is challenging for mobile robots or agents to approach free-standing conversational groups in a socially acceptable manner. It requires the robot or agent to plan trajectories that avoid collisions with people and consider the perception of group members to make them feel comfortable. Previous methods are mostly procedural with handcrafted features that limit the realism and adaptation of the simulation. In this thesis, Human-Robot/Agent Interaction is investigated at multiple levels, including individuals, crowds, and small groups. Firstly, this thesis is an exploration of proxemics in dyadic interactions in virtual environments. It investigates the impact of various embodiments on human perception and sensitivities. A related toolkit is developed as a foundation for simulating virtual characters in the subsequent research. Secondly, this thesis extends proxemics to crowd simulation and trajectory prediction by proposing neighbor perception models. It then focuses on group interactions in which robots/agents approach small groups in order to join them. To address the challenges above, novel procedural models based on social space and machine learning models, including generative adversarial neural networks, state refinement LSTM, reinforcement learning, and imitation learning, are proposed to generate approach behaviors. A novel dataset of full-body motion-captured markers was also collected in order to support machine learning approaches. Finally, these methods are evaluated in scenarios involving humans, virtual agents, and physical robots. ; QC 20201208


    Access

    Download


    Export, share and cite



    Title :

    Simulating Group Interactions through Machine Learning and Human Perception


    Contributors:

    Publication date :

    2020-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Machine Learning System For Indolence Perception

    Udhaya Sankar, S M / Kumar, N Jagadish / Dhinakaran, D et al. | IEEE | 2023



    Learning Gaze Behaviors for Balancing Participation in Group Human-Robot Interactions

    Gillet, Sarah / Parreira, Maria Teresa / Vázquez, Marynel et al. | BASE | 2022

    Free access

    THE EUROPEAN CANSAT COMPETITION – STEM LEARNING THROUGH SIMULATING A SATELLITE

    Sanchez, Elsa Alfonso / Talevi, Monica / Marée, Hugo et al. | TIBKAT | 2021


    Machine learning based prediction of human interactions with autonomous vehicles

    ANTHONY SAMUEL ENGLISH / MISRA KSHITIJ / FALLER AVERY WAGNER | European Patent Office | 2021

    Free access