Skip to main content
To KTH's start page To KTH's start page

Simulating Group Interactions through Machine Learning and Human Perception

Time: Mon 2021-01-25 10.00

Location: zoom link for online defense (English)

Subject area: Computer Science

Doctoral student: Fangkai Yang , Beräkningsvetenskap och beräkningsteknik (CST)

Opponent: Séverin Lemaignan, University of the West of England, Bristol

Supervisor: Christopher Peters, Beräkningsvetenskap och beräkningsteknik (CST)

Export to calendar


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.