Evaluating the Effect of Saliency Detection and Attention Manipulation in Human-Robot Interaction

The ability to share the attention with another individual is essential for having intuitive interaction. Two relatively simple, but important prerequisites for this, saliency detection and attention manipulation by the robot, are identified in the first part of the paper. By creating a saliency based attentional model combined with a robot ego-sphere and by adopting attention manipulation skills, the robot can engage in an interaction with a human and start an interaction game including objects as a first step towards a joint attention. We set up an interaction experiment in which participants could physically interact with a humanoid robot equipped with mechanisms for saliency detection and attention manipulation. We tested our implementation in four combinations of activated parts of the attention system, which resulted in four different behaviours. Our aim was to identify those physical and behavioural characteristics that need to be emphasised when implementing attentive mechanisms in robots, and to measure the user experience when interacting with a robot equipped with attentive mechanisms. We adopted two techniques for evaluating saliency detection and attention manipulation mechanisms in human-robot This work has been financed by the EU funded Initial Training Network (ITN) in the Marie-Curie People Programme (FP7): INTRO (INTeractive RObotics research network), grant agreement no.: 238486. G. Schillaci ( ) · S. Bodiroža · V.V. Hafner Cognitive Robotics Group, Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany e-mail: [email protected] S. Bodiroža e-mail: [email protected] V.V. Hafner e-mail: [email protected] interaction: user experience as measured by qualitative and quantitative questions in questionnaires and proxemics estimated from recorded videos of the interactions. The robot’s level of interactiveness has been found to be positively correlated with user experience factors like excitement and robot factors like lifelikeness and intelligence, suggesting that robots must give as much feedback as possible in order to increase the intuitiveness of the interaction, even when performing only attentive behaviours. This was confirmed also by proxemics analysis: participants reacted more frenetically when the interaction was perceived as less satisfying. Improving the robot’s feedback capability could increase user satisfaction and decrease the probability of unexpected or incomprehensible user movements. Finally, multi-modal interaction (through arm and head movements) increased the level of interactiveness perceived by participants. Positive correlation has been found between the elegance of robot movements and user satisfaction.