research-article

Robot self-initiative and personalization by learning through repeated interactions

Authors:

Manuel C. LopesAuthors Info & Claims

HRI '11: Proceedings of the 6th international conference on Human-robot interaction

Pages 433 - 440

https://doi.org/10.1145/1957656.1957814

Published: 06 March 2011 Publication History

Abstract

We have developed a robotic system that interacts with the user, and through repeated interactions, adapts to the user so that the system becomes semi-autonomous and acts proactively. In this work we show how to design a system to meet a user's preferences, show how robot pro-activity can be learned and provide an integrated system using verbal instructions. All these behaviors are implemented in a real platform that achieves all these behaviors and is evaluated in terms of user acceptability and efficiency of interaction.

References

[1]

P. Abbeel and A. Y. Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the 21st International Conference on Machine Learning (ICML'04), pages 1--8, 2004.

Digital Library

[2]

B. Argall, S. Chernova, and M. Veloso. A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469--483, 2009.

Digital Library

[3]

S. Chernova and M. Veloso. Learning equivalent action choices from demonstration. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 1216--1221. IEEE, 2008.

[4]

M. desJardins, E. Eaton, and K. Wagstaff. Learning user preferences for sets of objects. In Proceedings of the 23rd international conference on Machine learning, page 280. ACM, 2006.

Digital Library

[5]

T. W. Fong, I. Nourbakhsh, and K. Dautenhahn. A survey of socially interactive robots. Robotics and Autonomous Systems, 2003.

[6]

D. Grollman and O. Jenkins. Dogged learning for robots. In Robotics and Automation, 2007 IEEE International Conference on, pages 2483--2488, 2007.

[7]

Y. Guo and C. Gomes. Learning optimal subsets with implicit user preferences. In Proceedings of the 21st international joint conference on Artifical intelligence, pages 1052--1057, 2009.

Digital Library

[8]

B. Hardin and M. Goodrich. On using mixed-initiative control: a perspective for managing large-scale robotic teams. In Proceedings of the 4th ACM/IEEE international conference on Human robot interaction, pages 165--172. ACM, 2009.

Digital Library

[9]

R. Jaulmes, J. Pineau, and D. Precup. Active learning in partially observable markov decision processes. In NIPS Workshop on Value of Information in Inference, Learning and Decision-Making, 2005.

Digital Library

[10]

S. Kirkpatrick, C. Gelatt Jr, and M. Vecchi. Optimization by simulated annealing. Science, 220(4598):671, 1983.

[11]

W. B. Knox and P. Stone. Combining manual feedback with subsequent mdp reward signals for reinforcement learning. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'10), pages 5--12, 2010.

Digital Library

[12]

C. Lachi, G. Teti, G. Tambumni, E. Datteri, and P. Dario. Adaptable semi-autonomy in personal robots. In IEEE International Worksop on Robot and Human Interactive Communication, 2001.

[13]

S. LaValle. Planning algorithms. Cambridge Univ Pr, 2006.

Digital Library

[14]

M. Lopes, F. Melo, L. Montesano, and J. Santos-Victor. Abstraction levels for robotic imitation: Overview and computational approaches. In O. Sigaud and J. Peters, editors, From Motor to Interaction Learning in Robots. Springer, 2009.

[15]

M. Lopes, F. S. Melo, and L. Montesano. Active learning for reward estimation in inverse reinforcement learning. In European Conference on Machine Learning (ECML/PKDD), Bled, Slovenia, 2009.

Digital Library

[16]

L. Montesano and M. Lopes. Learning grasping affordances from local visual descriptors. In IEEE 8TH International Conference on Development and Learning, China, 2009.

Digital Library

[17]

Nuance. Nuance Speech Recognition System Version 8.5, 2004.

[18]

S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, second edition, 2003.

Digital Library

[19]

K. Severinson-Eklundh, A. Green, and H. Hüttenrauch. Social and collaborative aspects of interaction with a service robot. Robotics and Autonomous Systems, 42(3-4):223--234, 2003.

[20]

D. Silver, J. A. Bagnell, and A. Stentz. Learning from demonstration for autonomous navigation in complex unstructured terrain. International Journal of Robotics Research, 29(1):1565--1592, October 2010.

Digital Library

[21]

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

Digital Library

[22]

C. Zhu, R. Byrd, P. Lu, and J. Nocedal. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software, 23(4):550--560, 1997.

Digital Library

[23]

J. Zhu and T. Hastie. Kernel logistic regression and the import vector machine. In Adv. Neural Information Proc. Systems, pages 1081--1088, 2002.

Cited By

Abe NHu YBenallegue MYamanobe NVenture GYoshida E(2024)Human Understanding and Perception of Unanticipated Robot Action in the Context of Physical InteractionACM Transactions on Human-Robot Interaction10.1145/364345813:1(1-26)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3643458
Nayeem JTaj MMahmud MHossain FArabi A(2022)Implementation of Computer Vision in Low Fidelity Robots: Analysis, Challenges, and Design Implications2022 9th NAFOSTED Conference on Information and Computer Science (NICS)10.1109/NICS56915.2022.10013428(339-344)Online publication date: 31-Oct-2022
https://doi.org/10.1109/NICS56915.2022.10013428
Xie LLiu CLi D(2022)Proactivity or passivity? An investigation of the effect of service robots’ proactive behaviour on customer co-creation intentionInternational Journal of Hospitality Management10.1016/j.ijhm.2022.103271106(103271)Online publication date: Sep-2022
https://doi.org/10.1016/j.ijhm.2022.103271
Show More Cited By

Index Terms

Robot self-initiative and personalization by learning through repeated interactions
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

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Published In

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

General Chairs:
Aude Billard
EPFL, Switzerland
,
Peter Kahn
University of Washington, USA
,
Program Chairs:
Julie A. Adams
Vanderbilt University, USA
,
Greg Trafton
US Naval Research Laboratories, USA

Copyright © 2011 ACM.

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

Publication History

Published: 06 March 2011

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HRI'11

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HRI'11: International Conference on Human-Robot Interaction

March 6 - 9, 2011

Lausanne, Switzerland

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

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Cited By

Abe NHu YBenallegue MYamanobe NVenture GYoshida E(2024)Human Understanding and Perception of Unanticipated Robot Action in the Context of Physical InteractionACM Transactions on Human-Robot Interaction10.1145/364345813:1(1-26)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3643458
Nayeem JTaj MMahmud MHossain FArabi A(2022)Implementation of Computer Vision in Low Fidelity Robots: Analysis, Challenges, and Design Implications2022 9th NAFOSTED Conference on Information and Computer Science (NICS)10.1109/NICS56915.2022.10013428(339-344)Online publication date: 31-Oct-2022
https://doi.org/10.1109/NICS56915.2022.10013428
Xie LLiu CLi D(2022)Proactivity or passivity? An investigation of the effect of service robots’ proactive behaviour on customer co-creation intentionInternational Journal of Hospitality Management10.1016/j.ijhm.2022.103271106(103271)Online publication date: Sep-2022
https://doi.org/10.1016/j.ijhm.2022.103271
Ruiz Garcia MRauch EVidoni RMatt D(2021)AI and ML for Human-Robot Cooperation in Intelligent and Flexible ManufacturingImplementing Industry 4.0 in SMEs10.1007/978-3-030-70516-9_3(95-127)Online publication date: 9-May-2021
https://doi.org/10.1007/978-3-030-70516-9_3
Baroud HChatterjee SBrigantic RWaterworth A(2021)Risk Analysis Methods in Resilience Modeling: An Overview of Critical Infrastructure ApplicationsApplied Risk Analysis for Guiding Homeland Security Policy10.1002/9781119287490.ch14(357-379)Online publication date: 28-Jan-2021
https://doi.org/10.1002/9781119287490.ch14
Knijnenburg BHubig N(2020)Human-Centric Preference Modeling for Virtual AgentsProceedings of the 20th ACM International Conference on Intelligent Virtual Agents10.1145/3383652.3423909(1-3)Online publication date: 20-Oct-2020
https://dl.acm.org/doi/10.1145/3383652.3423909
Tan HZhao YLi SWang WZhu MHong JYuan X(2020)Relationship between social robot proactive behavior and the human perception of anthropomorphic attributesAdvanced Robotics10.1080/01691864.2020.183169934:20(1324-1336)Online publication date: 12-Oct-2020
https://doi.org/10.1080/01691864.2020.1831699
Steil JMaier G(2020)Kollaborative Roboter: universale Werkzeuge in der digitalisierten und vernetzten ArbeitsweltHandbuch Gestaltung digitaler und vernetzter Arbeitswelten10.1007/978-3-662-52979-9_15(323-346)Online publication date: 15-May-2020
https://doi.org/10.1007/978-3-662-52979-9_15
Liu RZhang X(2019)A review of methodologies for natural-language-facilitated human–robot cooperationInternational Journal of Advanced Robotic Systems10.1177/172988141985140216:3Online publication date: 16-Jun-2019
https://doi.org/10.1177/1729881419851402
Moharana SPanduro ALee HRiek L(2019)Robots for Joy, Robots for Sorrow: Community Based Robot Design for Dementia Caregivers2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI.2019.8673206(458-467)Online publication date: Mar-2019
https://doi.org/10.1109/HRI.2019.8673206
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