NAO reading a thesis on robot body language


  • Natural Language Interaction
  • User Centred Design
  • Serious / Educational Games
  • Human Factors
  • Bunnies


  • Personal Assistant for a Healthy Lifestyle

    Personal Assistant for a Healthy Lifestyle

    Strategic Behaviour Planning for a Social Robot in Children's Education

    The PAL (Personal  Assistant for healthy Lifestyle) project is a 4 year H2020 project with the research partners TNO (coordinator), DFKI, FCSR, Imperial and Delft University of Technology, next to end-users (the hospitals Gelderse Vallei and Meander, and the Diabetics Associations of Netherlands and Italy), and SME’s (Mixel and Produxi).  PAL will use, refine and extend the knowledge-base and support models of earlier project by partners to improve child’s diabetes regimen by assisting the child, health professional and parent. The PAL system will be composed of a social robot (NAO), its (mobile) avatar, and an extendable set of (mobile) health applications (diabetes diary, educational quizzes, sorting games, etc.), which all connect to a common knowledge-base and reasoning mechanism.

    Project website

  • Commit - Interaction for Universal Access

    Commit - Interaction for Universal Access

    Social Signals in Interaction Between Humans and Artificial Agents

    The IUALL project foundation is the idea that anybody should be able to interact with computer systems. The project challenge is to investigate social intelligent technology and how to design them for Universal Access; include people with special needs such as children, elderly and people with disabilities.

    I participated in the work package focused on interpersonal and contextual aspects of turn-taking and floor management in conversations were are build models of turn-taking and floor management for artificial conversational agents that display appropriate behaviours when they are involved in a conversation.

    Specifically I studied non-verbal behaviour in human-human conversations (police interviewing): how interlocutors by means of gestures, head movements, backchannels etc. express their stance towards the other and related turn management. We identified factors (agent characteristics, conversational content, timings, tasks, scenario, role, personality) influencing how an interlocutor is perceived and in a perception study we investigated how turn-taking patterns influence the interpersonal stance of an embodied conversational agent as perceived by a human.

    Project website
    Research Report
    Master Thesis

You never know how a rabbit catches a cow.

~ Dutch proverb ~

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