Hand in Hand with Robots: Differences Between Experienced and Naive Users in Human-Robot Handover Scenarios
Abstract: Service robots are expected to closely interact with humans in the near future. Their tasks often include delivering and taking objects. Thus, handover scenarios play an important role in human-robot-interaction. A lot of work in this field of research focuses on speed, accuracy and predictability of the robot’s movement during object handover. Those robots need to closely interact with naive users and not only experts. In order to evaluate handover interaction performance between human and robot a force measurement based approach was implemented on the humanoid robot Floka. Different gestures with the second arm were added to analyze the influence on synchronization, predictability, and human acceptance. In this paper we present a study where users with different levels of experience were asked to help the robot to learn new objects. We evaluated the impact of previous knowledge with robots on handover interactions. Disparities in timing, distance, and applied force during handover could be observed. We present an automated annotation pipeline for human-robot-interaction that will be used in future studies. While the commonly used force measurement based approach proved to be a valid starting point, our results show that naive user interaction could benefit from better anticipation.
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