Investigating Human-Human Approach and Hand-Over

  • Patrizia Basili
  • Markus Huber
  • Thomas Brandt
  • Sandra Hirche
  • Stefan Glasauer
Part of the Cognitive Systems Monographs book series (COSMOS, volume 6)


Humans interact safely, effortlessly, and intuitively with each other. An efficient robot assistant should thus be able to interact in the same way. This requires not only that the robot can react appropriately to human behaviour, but also that robotic behaviour can be understood intuitively by the human partners. The latter can be achieved by the robot mimicking certain aspects of human behaviour so that the human partner can more easily infer the intentions of the robot. Here we investigate a simple interaction scenario, approach and hand-over, to gain better understanding of the behavioural patterns in human-human interactions. In our experiment, one human subject, holding an object, approached another subject with the goal to hand over the object. Head and object positions were measured with a motion tracking device to analyse the behaviour of the approaching human. Interaction indicated by lifting the object in order to prepare for hand-over started approximately 1.2 s before the actual hand-over. Interpersonal distance varied considerably between subjects with an average of 1.16 m. To test whether the behavioural patterns observed depended on two humans being present, we replaced in a second experiment the receiving subject with a table. We found that the behaviour of the transferring subject was very similar in both scenarios. Thus, the presence of the receiving subject plays a minor role in determining parameters such as start of interaction or interaction distance. We aim to implement and test the parameters derived experimentally in a robotic assistant to improve and facilitate human-robot interaction.


Social Robot Human Partner Interaction Scenario Interpersonal Distance Standing Person 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrizia Basili
    • 1
  • Markus Huber
    • 1
  • Thomas Brandt
    • 2
  • Sandra Hirche
    • 3
  • Stefan Glasauer
    • 1
  1. 1.Center for Sensorimotor Research, Institute of Clinical NeurosciencesLudwig-Maximilians-Universität München 
  2. 2.Chair of Clinical NeurosciencesLudwig-Maximilians-Universität München 
  3. 3.Institute of Automatic Control EngineeringTechnische Universität München 

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