In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naïve subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turntaking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.
The recent ubiquity of high-framerate (120 fps and higher) handheld cameras creates the opportunity to study human grasping at a greater level of detail than normal speed cameras allow. We first collected 91 slow-motion interactions with objects in a convenience store setting. We then annotated the actions through the lenses of various existing manipulation taxonomies. We found manipulation, particularly the process of forming a grasp, is complicated and proceeds quickly. Our dataset shows that there are many ways that people deal with clutter in order to form a strong grasp of an object. It also reveals several errors and how people recover from them. Though annotating motions in detail is time-consuming, the annotation systems we used nevertheless leave out important aspects of understanding manipulation actions, such as how the environment is functioning as a "finger" of sorts, how different parts of the hand can be involved in different grasping tasks, and high-level intent.
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