abstract

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Using Human Reinforcement Learning Models to Improve Robots as Teachers

Authors:

Sayanti Roy,

Emily Kieson,

Charles Abramson,

Christopher CrickAuthors Info & Claims

HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

Pages 225 - 226

https://doi.org/10.1145/3173386.3177074

Published: 01 March 2018 Publication History

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Abstract

Robotic teaching has not received nearly as much research attention as robotic learning. In this research, we used the humanoid robot Baxter to provide feedback and positive reinforcement to human participants attempting to achieve a complex task. Our robot autonomously casts the teaching problem as one that invokes the exploration/exploitation tradeoff to understand the cognitive strategy of its human partner and develop an effective motivational approach. We compare our learned reinforcement model with a baseline non-reinforcement approach and with a random reinforcer.

References

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A. L. Thomaz and C. Breazeal, “Teachable robots: Understanding human behavior to build more effective robot learners,” Artificial Intelligence, vol. 172, pp. 716--737, 2008.

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[3]

S. Roy, E. Kieson, C. Abramson, and C. Crick, “Semantic structure for robotic teaching and learning,” in Proceedings of the 26th IEEE International Symposium on Robotics and Human Interactive Communication (RO-MAN), 2017.

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S. Roy, H. Maske, G. Chowdhary, and C. Crick, “Teaching and learning using semantic labels,” in Proceedings of the 12th ACM/IEEE Conference on Human-Robot Interaction (HRI), 2017.

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Cited By

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Park YRoy S(2024)I have other tasks to do… Analyzing Human Perception of Robots Practising Ruthless Prioritization2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731169(1471-1476)Online publication date: 26-Aug-2024
https://doi.org/10.1109/RO-MAN60168.2024.10731169
Chhetri CGenaro Motti V(2022)User-Centric Privacy Controls for Smart HomesProceedings of the ACM on Human-Computer Interaction10.1145/35557696:CSCW2(1-36)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555769
Candello HPinhanez CMuller MWessel M(2022)Unveiling Practices of Customer Service Content Curators of Conversational AgentsProceedings of the ACM on Human-Computer Interaction10.1145/35557686:CSCW2(1-33)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555768
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Index Terms

Using Human Reinforcement Learning Models to Improve Robots as Teachers
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Cognitive robotics
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
        Apprenticeship learning
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. Interaction paradigms
      1. Collaborative interaction

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Information & Contributors

Information

Published In

HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

March 2018

431 pages

ISBN:9781450356152

DOI:10.1145/3173386

General Chairs:
Takayuki Kanda
ATR, Japan
,
Selma Ŝabanović
Indiana University Bloomington, USA
,
Program Chairs:
Guy Hoffman
Cornell University, USA
,
Adriana Tapus
ENSTA-ParisTech, France

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2018

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Funding Sources

National Science Foundation

Conference

HRI '18

Sponsor:

SIGAI
SIGCHI
IEEE-RAS

HRI '18: ACM/IEEE International Conference on Human-Robot Interaction

March 5 - 8, 2018

IL, Chicago, USA

Acceptance Rates

HRI '18 Paper Acceptance Rate 49 of 206 submissions, 24%;

Overall Acceptance Rate 192 of 519 submissions, 37%

Upcoming Conference

HRI '25

Sponsor:
sigai
sigai

ACM/IEEE International Conference on Human-Robot Interaction

March 4 - 6, 2025

Melbourne , VIC , Australia

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Cited By

View all

Park YRoy S(2024)I have other tasks to do… Analyzing Human Perception of Robots Practising Ruthless Prioritization2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)10.1109/RO-MAN60168.2024.10731169(1471-1476)Online publication date: 26-Aug-2024
https://doi.org/10.1109/RO-MAN60168.2024.10731169
Chhetri CGenaro Motti V(2022)User-Centric Privacy Controls for Smart HomesProceedings of the ACM on Human-Computer Interaction10.1145/35557696:CSCW2(1-36)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555769
Candello HPinhanez CMuller MWessel M(2022)Unveiling Practices of Customer Service Content Curators of Conversational AgentsProceedings of the ACM on Human-Computer Interaction10.1145/35557686:CSCW2(1-33)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555768
Wyche S(2022)Reimagining the Mobile Phone: Investigating Speculative Approaches to Design in Human-Computer Interaction for Development (HCI4D)Proceedings of the ACM on Human-Computer Interaction10.1145/35556486:CSCW2(1-27)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555648
Matatov HNaaman MAmir O(2022)Stop the [Image] StealProceedings of the ACM on Human-Computer Interaction10.1145/35555996:CSCW2(1-24)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555599
Escobar-Planas MCharisi VGomez E(2022)"That Robot Played with Us!" Children's Perceptions of a Robot after a Child-Robot Group InteractionProceedings of the ACM on Human-Computer Interaction10.1145/35551186:CSCW2(1-23)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555118
Kaur MParkin SJanssen MFiebig T(2022)"I needed to solve their overwhelmness": How System Administration Work was Affected by COVID-19Proceedings of the ACM on Human-Computer Interaction10.1145/35551156:CSCW2(1-30)Online publication date: 11-Nov-2022
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Ahlberg SAxelsson AYu PCortez WGao YGhadirzadeh ACastellano GKragic DSkantze GDimarogonas D(2021)Co-adaptive Human–Robot Cooperation: Summary and ChallengesUnmanned Systems10.1142/S230138502250011X10:02(187-203)Online publication date: 17-Sep-2021
https://doi.org/10.1142/S230138502250011X
Ahmad MGao YAlnajjar FShahid SMubin O(2021)Emotion and memory model for social robots: a reinforcement learning based behaviour selectionBehaviour & Information Technology10.1080/0144929X.2021.197738941:15(3210-3236)Online publication date: 27-Sep-2021
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Roy SKieson EAbramson CCrick C(2019)Mutual Reinforcement Learning with Robot Trainers2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI.2019.8673284(572-573)Online publication date: Mar-2019
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