research-article

Transparent active learning for robots

Authors:

Andrea L. ThomazAuthors Info & Claims

HRI '10: Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction

Pages 317 - 324

Published: 02 March 2010 Publication History

Abstract

This research aims to enable robots to learn from human teachers. Motivated by human social learning, we believe that a transparent learning process can help guide the human teacher to provide the most informative instruction. We believe active learning is an inherently transparent machine learning approach because the learner formulates queries to the oracle that reveal information about areas of uncertainty in the underlying model. In this work, we implement active learning on the Simon robot in the form of nonverbal gestures that query a human teacher about a demonstration within the context of a social dialogue. Our preliminary pilot study data show potential for transparency through active learning to improve the accuracy and efficiency of the teaching process. However, our data also seem to indicate possible undesirable effects from the human teacher's perspective regarding balance of the interaction. These preliminary results argue for control strategies that balance leading and following during a social learning interaction.

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Cited By

Gregory JSahu DLancaster ESanchez FRocks TKaukeinen BFink JGupta S(2022)Active Learning for Testing and Evaluation in Field Robotics: A Case Study in Autonomous, Off-Road Navigation2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9812453(8217-8223)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9812453
Bullard KSchroecker YChernova S(2019)Active learning within constrained environments through imitation of an expert questionerProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367323(2045-2052)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367323
Paul RArkin JAksaray DRoy NHoward T(2018)Efficient grounding of abstract spatial concepts for natural language interaction with robot platformsInternational Journal of Robotics Research10.1177/027836491877762737:10(1269-1299)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1177/0278364918777627
Show More Cited By

Index Terms

Transparent active learning for robots
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
2. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Robotic planning
    2. Planning and scheduling
      1. Robotic planning
  2. Machine learning

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Information

Published In

cover image ACM Conferences

HRI '10: Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction

March 2010

400 pages

ISBN:9781424448937

General Chairs:
Pamela Hinds
Stanford University, USA
,
Hiroshi Ishiguro
Osaka University, Japan
,
Program Chairs:
Takayuki Kanda
ATR, Japan
,
Peter Kahn
University of Washington, USA

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IEEE Press

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Published: 02 March 2010

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HRI 10

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HRI 10: International Conference on Human Robot Interaction

March 2 - 5, 2010

Osaka, Japan

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HRI '10 Paper Acceptance Rate 26 of 124 submissions, 21%;

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

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Cited By

Gregory JSahu DLancaster ESanchez FRocks TKaukeinen BFink JGupta S(2022)Active Learning for Testing and Evaluation in Field Robotics: A Case Study in Autonomous, Off-Road Navigation2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9812453(8217-8223)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9812453
Bullard KSchroecker YChernova S(2019)Active learning within constrained environments through imitation of an expert questionerProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367323(2045-2052)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367323
Paul RArkin JAksaray DRoy NHoward T(2018)Efficient grounding of abstract spatial concepts for natural language interaction with robot platformsInternational Journal of Robotics Research10.1177/027836491877762737:10(1269-1299)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1177/0278364918777627
Racca MKyrki VKanda TŜabanović SHoffman GTapus A(2018)Active Robot Learning for Temporal Task ModelsProceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3171221.3171241(123-131)Online publication date: 26-Feb-2018
https://dl.acm.org/doi/10.1145/3171221.3171241
Li GWhiteson SKnox WHung H(2016)Using informative behavior to increase engagement while learning from human rewardAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9308-230:5(826-848)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10458-015-9308-2
Breazeal CDePalma NOrkin JChernova SJung M(2013)Crowdsourcing human-robot interactionJournal of Human-Robot Interaction10.5898/JHRI.2.1.Breazeal2:1(82-111)Online publication date: 27-Feb-2013
https://dl.acm.org/doi/10.5898/JHRI.2.1.Breazeal
Li GHung HWhiteson SKnox WGini MShehory OIto TJonker C(2013)Using informative behavior to increase engagement in the tamer frameworkProceedings of the 2013 international conference on Autonomous agents and multi-agent systems10.5555/2484920.2485064(909-916)Online publication date: 6-May-2013
https://dl.acm.org/doi/10.5555/2484920.2485064
Rosenthal SSkaff SVeloso MBohus DHorvitz EKuzuoka HEvers VImai MForlizzi J(2013)Execution memory for grounding and coordinationProceedings of the 8th ACM/IEEE international conference on Human-robot interaction10.5555/2447556.2447643(213-214)Online publication date: 3-Mar-2013
https://dl.acm.org/doi/10.5555/2447556.2447643
Cuayáhuitl Hvan Otterlo MDethlefs NFrommberger L(2013)Machine learning for interactive systems and robotsProceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication10.1145/2493525.2493530(19-28)Online publication date: 4-Aug-2013
https://dl.acm.org/doi/10.1145/2493525.2493530
Sekmen AChalla P(2013)Assessment of adaptive human-robot interactionsKnowledge-Based Systems10.1016/j.knosys.2013.01.00342(49-59)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.knosys.2013.01.003
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