The where of handovers by humans: Effect of partner characteristics, distance and visual feedback

Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of ‘givers’ and ‘receivers’ and discuss how the behavior of the same individual changes depending on his role in the handover.

[1]  Giuseppe di Pellegrino,et al.  Neuropsychological Evidence of an Integrated Visuotactile Representation of Peripersonal Space in Humans , 1998, Journal of Cognitive Neuroscience.

[2]  M. Emmelmann,et al.  Influence of velocity on the handover delay associated with a radio-signal-measurement-based handover decision , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[3]  Alois Knoll,et al.  Evaluation of a novel biologically inspired trajectory generator in human-robot interaction , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[4]  B. Malle,et al.  Social dominance orientation: A personality variable predicting social and political attitudes. , 1994 .

[5]  Gowrishankar Ganesh,et al.  Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans , 2017, eNeuro.

[6]  Siddhartha S. Srinivasa,et al.  Eye-Hand Behavior in Human-Robot Shared Manipulation , 2018, 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[7]  HeWuwei,et al.  Improving human-robot object exchange by online force classification , 2015, HRI 2015.

[8]  A. Caramazza,et al.  Domain-Specific Knowledge Systems in the Brain: The Animate-Inanimate Distinction , 1998, Journal of Cognitive Neuroscience.

[9]  Jianwei Zhang,et al.  Reusability-based Semantics for Grasp Evaluation in Context of Service Robotics , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[10]  J. Cyriax,et al.  Manipulation , 2018, Encyclopedia of Evolutionary Psychological Science.

[11]  Siddhartha S. Srinivasa,et al.  Human preferences for robot-human hand-over configurations , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  A. Berti,et al.  When Far Becomes Near: Remapping of Space by Tool Use , 2000, Journal of Cognitive Neuroscience.

[13]  Siddhartha S. Srinivasa,et al.  Toward seamless human-robot handovers , 2013, Journal of Human-Robot Interaction.

[14]  Hiroki Nakamoto,et al.  Prediction error induced motor contagions in human behaviors , 2017, bioRxiv.

[15]  Rieko Osu,et al.  CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm , 2008, The Journal of Neuroscience.

[16]  Masayuki Inaba,et al.  Characterization of handover orientations used by humans for efficient robot to human handovers , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  R. J. Beers,et al.  Motor Learning Is Optimally Tuned to the Properties of Motor Noise , 2009, Neuron.

[18]  Alois Knoll,et al.  Human-robot interaction in handing-over tasks , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[19]  Véronique Perdereau,et al.  Human–Human Handover Tasks and How Distance and Object Mass Matter , 2017, Perceptual and motor skills.

[20]  Alin Albu-Schäffer,et al.  A versatile biomimetic controller for contact tooling and haptic exploration , 2012, 2012 IEEE International Conference on Robotics and Automation.

[21]  G Ganesh,et al.  Immediate tool incorporation processes determine human motor planning with tools , 2014, Nature Communications.

[22]  Elizabeth A. Croft,et al.  Grip forces and load forces in handovers: Implications for designing human-robot handover controllers , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[23]  Jérémy Bourgeois,et al.  Costs and benefits of tool-use on the perception of reachable space. , 2014, Acta psychologica.

[24]  Rachid Alami,et al.  A Human-Aware Manipulation Planner , 2012, IEEE Transactions on Robotics.

[25]  D. Wolpert,et al.  Principles of sensorimotor learning , 2011, Nature Reviews Neuroscience.

[26]  Stefano Caselli,et al.  An Affordance Sensitive System for Robot to Human Object Handover , 2014, Int. J. Soc. Robotics.

[27]  Daniel Sidobre,et al.  Improving human-robot object exchange by online force classification , 2015, HRI 2015.

[28]  Mitsuo Kawato,et al.  Physically interacting individuals estimate the partner’s goal to enhance their movements , 2017, Nature Human Behaviour.

[29]  Hikaru Inooka,et al.  Motion planning for hand-over between human and robot , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[30]  G. Ganesh,et al.  Publisher Correction: Immediate tool incorporation processes determine human motor planning with tools , 2018, Nature communications.

[31]  Sami Haddadin,et al.  Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification , 2018, IEEE Transactions on Robotics.