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Using spatial and temporal contrast for fluent robot-human hand-overs

Published: 06 March 2011 Publication History

Abstract

For robots to get integrated in daily tasks assisting humans, robot-human interactions will need to reach a level of fluency close to that of human-human interactions. In this paper we address the fluency of robot-human hand-overs. From an observational study with our robot HERB, we identify the key problems with a baseline hand-over action. We find that the failure to convey the intention of handing over causes delays in the transfer, while the lack of an intuitive signal to indicate timing of the hand-over causes early, unsuccessful attempts to take the object. We propose to address these problems with the use of spatial contrast, in the form of distinct hand-over poses, and temporal contrast, in the form of unambiguous transitions to the hand-over pose. We conduct a survey to identify distinct hand-over poses, and determine variables of the pose that have most communicative potential for the intent of handing over. We present an experiment that analyzes the effect of the two types of contrast on the fluency of hand-overs. We find that temporal contrast is particularly useful in improving fluency by eliminating early attempts of the human.

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cover image ACM Conferences
HRI '11: Proceedings of the 6th international conference on Human-robot interaction
March 2011
526 pages
ISBN:9781450305617
DOI:10.1145/1957656
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • RA: IEEE Robotics and Automation Society
  • Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc
  • The Association for the Advancement of Artificial Intelligence (AAAI)
  • IEEE Systems, Man and Cybernetics Society

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Association for Computing Machinery

New York, NY, United States

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Published: 06 March 2011

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  1. fluency
  2. robot-human hand-overs

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Cited By

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  • (2024)Charting User Experience in Physical Human–Robot InteractionACM Transactions on Human-Robot Interaction10.1145/365905813:2(1-29)Online publication date: 28-Jun-2024
  • (2024)Analysis of Hesitation Behavior by Human Receiver toward Facing Motion of Robot during Handover2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731461(399-404)Online publication date: 26-Aug-2024
  • (2024)Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)10.1109/Humanoids58906.2024.10769881(173-180)Online publication date: 22-Nov-2024
  • (2024)Collaborative Robotics: A Survey From Literature and Practitioners PerspectivesJournal of Intelligent & Robotic Systems10.1007/s10846-024-02141-z110:3Online publication date: 6-Aug-2024
  • (2023)How to Communicate Robot Motion Intent: A Scoping ReviewProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580857(1-17)Online publication date: 19-Apr-2023
  • (2022)Robot, Pass Me the Tool: Handle Visibility Facilitates Task-oriented HandoversProceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction10.5555/3523760.3523797(256-264)Online publication date: 7-Mar-2022
  • (2022)I Let Go Now! Towards a Voice-User Interface for Handovers between Robots and Users with Full and Impaired SightRobotics10.3390/robotics1105011211:5(112)Online publication date: 15-Oct-2022
  • (2021)A Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion PredictionSensors10.3390/s2124822921:24(8229)Online publication date: 9-Dec-2021
  • (2021)Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and MetricsSensors10.3390/s2112411321:12(4113)Online publication date: 15-Jun-2021
  • (2020)Priming and Timing in Human-Robot InteractionsModelling Human Motion10.1007/978-3-030-46732-6_16(335-350)Online publication date: 10-Jul-2020
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