Human preferences for robot-human hand-over configurations

By Maya Cakmak, Siddhartha S. Srinivasa, Min Kyung Lee, Jodi L. Forlizzi, Sara B. Kiesler. 2011

175 citations recorded by the source corpus.

Handing over objects to humans is an essential capability for assistive robots. While there are infinite ways to hand an object, robots should be able to choose the one that is best for the human. In this paper we focus on choosing the robot and object configuration at which the transfer of the object occurs, i.e. the hand-over configuration. We advocate the incorporation of user preferences in choosing hand-over configurations. We present a user study in which we collect data on human preferences and a human-robot interaction experiment in which we compare hand-over configurations learned from human examples against configurations planned using a kinematic model of the human. We find that the learned configurations are preferred in terms of several criteria, however planned configurations provide better reachability. Additionally, we find that humans prefer hand-overs with default orientations of objects and we identify several latent variables about the robot's arm that capture significant human preferences. These findings point towards planners that can generate not only optimal but also preferable hand-over configurations for novel objects.

Subfields: Cognitive Neuroscience, Control and Systems Engineering, Social Psychology, Robotics & Control

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Citations in the corpus

Learning Dynamic Robot-to-Human Object Handover from Human Feedback (2016, background): “In the first stage, the giver approaches the taker and poses the object to get ready for handover [4, 20, 25].”
Implementation of a robot-human object handover controller on a compliant underactuated hand using joint position error measurements for grip force and load force estimations (2014, background): “studied how a robot should orient the object when handing it over [10], [11].”
Toward seamless human-robot handovers (2013, methodology): “To investigate the gap between handover conﬁgurations autonomously computed with these methods, and handover conﬁgurations demonstrated by humans, we conducted a study that compares the two approaches in Cakmak, Srinivasa, Lee, Forlizzi, and Kiesler (2011).”