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Human and robot perception in large-scale learning from demonstration

Published: 06 March 2011 Publication History

Abstract

We present a study of using a robotic learning from demonstration system capable of collecting large amounts of human-robot interaction data through a web-based interface. We examine the effect of different perceptual mappings between the human teacher and robot on the learning from demonstration. We show that humans are significantly more effective at teaching a robot to navigate a maze when presented with information that is limited to the robot's perception of the world, even though their task performance measurably suffers when contrasted with users provided with a natural and detailed raw video feed. Robots trained on such demonstrations learn more quickly, perform more accurately and generalize better. We also demonstrate a set of software tools for enabling internet-mediated human-robot interaction and gathering the large datasets that such crowdsourcing makes possible.

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Cited By

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  • (2023)Web Perspectives in Robotics ApplicationsACM SIGWEB Newsletter10.1145/3583849.35838532023:Winter(1-22)Online publication date: 3-Mar-2023
  • (2023)Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309481(1737-1744)Online publication date: 28-Aug-2023
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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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    Author Tags

    1. crowdsourcing
    2. interface design
    3. learning from demonstration

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    Overall Acceptance Rate 268 of 1,124 submissions, 24%

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    Cited By

    View all
    • (2023)Web Perspectives in Robotics ApplicationsACM SIGWEB Newsletter10.1145/3583849.35838532023:Winter(1-22)Online publication date: 3-Mar-2023
    • (2023)Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309481(1737-1744)Online publication date: 28-Aug-2023
    • (2023)Web Based Lidar Point Cloud Visualization and Teleoperation Tool for Robots2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307953(1-6)Online publication date: 6-Jul-2023
    • (2023)Super IntendoRobotics and Autonomous Systems10.1016/j.robot.2023.104397166:COnline publication date: 1-Aug-2023
    • (2022)Non-Dyadic Interaction: A Literature Review of 15 Years of Human-Robot Interaction Conference PublicationsACM Transactions on Human-Robot Interaction10.1145/348824211:2(1-32)Online publication date: 8-Feb-2022
    • (2021)Crowdsourced Evaluation of Robot Programming Environments: Methodology and ApplicationApplied Sciences10.3390/app11221090311:22(10903)Online publication date: 18-Nov-2021
    • (2021)Leveraging Human Perception in Robot Grasping and Manipulation Through Crowdsourcing and GamificationFrontiers in Robotics and AI10.3389/frobt.2021.6527608Online publication date: 29-Apr-2021
    • (2021)A Cloud-based Robot System for Long-term Interaction: Principles, Implementation, Lessons LearnedACM Transactions on Human-Robot Interaction10.1145/348158511:1(1-27)Online publication date: 18-Oct-2021
    • (2021)Enhancing Robot Perception in Grasping and Dexterous Manipulation through Crowdsourcing and Gamification2021 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48506.2021.9562114(2569-2575)Online publication date: 30-May-2021
    • (2021)Investigating the Effects of Robot Engagement Communication on Learning from DemonstrationInternational Journal of Social Robotics10.1007/s12369-021-00825-214:3(789-806)Online publication date: 8-Oct-2021
    • Show More Cited By

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