Article

What Am I Allowed to Do Here?: Online Learning of Context-Specific Norms by Pepper

Authors:

Alan R. WagnerAuthors Info & Claims

Social Robotics: 12th International Conference, ICSR 2020, Golden, CO, USA, November 14–18, 2020, Proceedings

Pages 220 - 231

https://doi.org/10.1007/978-3-030-62056-1_19

Published: 14 November 2020 Publication History

Abstract

Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the society. This paper presents a computational framework for learning contexts and the social norms present in a context in an online manner on a robot. The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts). The paper further utilizes Dempster-Schafer theory to model context-specific norms. After learning the scenes (contexts), we use active learning to learn related norms. We test our approach on the Pepper robot by taking it through different scene locations. Our results show that Pepper can learn different scenes and related norms simply by communicating with a human partner in an online manner.

References

[1]

Ayub, A., Fendley, C., Wagner, A.R.: Boundaryless online learning of indoor scenes by a robot, in Review, CoRL (2020)

[2]

Ayub, A., Wagner, A.R.: Centroid based concept learning for RGB-D indoor scene classification. arXiv:1911.00155 (2020)

[3]

Ayub, A., Wagner, A.R.: Cognitively-inspired model for incremental category learning using only a few examples in Review, Pattern Recognition (2020)

[4]

Ayub, A., Wagner, A.R.: Cognitively-inspired model for incremental learning using a few examples. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2020

[5]

Ayub, A., Wagner, A.R.: Online learning of objects through curiosity-driven active learning. In: IEEE RoMan Workshop on Lifelong Learning for Long-term Human-Robot Interaction (2020)

[6]

Ayub, A., Wagner, A.R.: Storing encoded episodes as concepts for continual learning. arXiv:2007.06637 (2020)

[7]

Ayub, A., Wagner, A.R.: Tell me what this is: few-shot incremental object learning by a robot. arXiv:2008.00819 (2020)

[8]

Kawewong, A., Pimup, R., Hasegawa, O.: Incremental learning framework for indoor scene recognition. In: AAAI (2013)

[9]

Krishnamoorthy, V., Luo, W., Lewis, M., Sycara, K.: A computational framework for integrating task planning and norm aware reasoning for social robots. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 282–287 (2018)

[10]

Paul, S., Bappy, J.H., Roy-Chowdhury, A.K.: Non-uniform subset selection for active learning in structured data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

[11]

Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420, June 2009

[12]

Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

[13]

Sarathy, V., Scheutz, M., Malle, B.F.: Learning behavioral norms in uncertain and changing contexts. In: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 000301–000306 (2017)

[14]

Sarathy, V., Scheutz, M., Kenett, Y., Allaham, M., Austerweil, J., Malle, B.: Mental representations and computational modeling of context-specific human norm systems. In: 39th Annual Meeting of the Cognitive Science Society (CogSci) (2017)

[15]

Shafer G A Mathematical Theory of Evidence 1976 Princeton Princeton University Press

[16]

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, September 2014

[17]

Tan, Z.X., Brawer, J., Scassellati, B.: That’s mine! Learning ownership relations and norms for robots. In: AAAI (2019)

[18]

Ullmann-Margalit E The Emergence of Norms 1976 Oxford Clarendon Press

[19]

Wang Z, Wang L, Wang Y, Zhang B, and Qiao YWeakly supervised patchnets: describing and aggregating local patches for scene recognitionIEEE Trans. Image Process.20172642028-20413636249

[20]

Wu, Y., et al.: Large scale incremental learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

[21]

Zhou B, Lapedriza A, Khosla A, Oliva A, and Torralba A Places: a 10 million image database for scene recognition IEEE Trans. Pattern Anal. Mach. Intell. 2017 40 6 1452-1464

Cited By

Ayub ADe Francesco ZMehta JYaakoub Agha KHolthaus PNehaniv CDautenhahn K(2024)A Human-Centered View of Continual Learning: Understanding Interactions, Teaching Patterns, and Perceptions of Human Users Toward a Continual Learning Robot in Repeated InteractionsACM Transactions on Human-Robot Interaction10.1145/365911013:4(1-39)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3659110
Ayub AFendley CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Few-shot continual active learning by a robotProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602489(30612-30624)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602489

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Social Robotics: 12th International Conference, ICSR 2020, Golden, CO, USA, November 14–18, 2020, Proceedings

Nov 2020

726 pages

ISBN:978-3-030-62055-4

DOI:10.1007/978-3-030-62056-1

Editors:
Alan R. Wagner
Pennsylvania State University, University Park, PA, USA
,
David Feil-Seifer
University of Nevada Reno, Reno, NV, USA
,
Kerstin S. Haring
University of Denver, Denver, CO, USA
,
Silvia Rossi
University of Naples Federico II, Naples, Italy
,
Thomas Williams
Colorado School of Mines, Golden, CO, USA
,
Hongsheng He
Wichita State University, Wichita, KS, USA
,
Shuzhi Sam Ge
National University of Singapore, Singapore, Singapore

© Springer Nature Switzerland AG 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 November 2020

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Cited By

Ayub ADe Francesco ZMehta JYaakoub Agha KHolthaus PNehaniv CDautenhahn K(2024)A Human-Centered View of Continual Learning: Understanding Interactions, Teaching Patterns, and Perceptions of Human Users Toward a Continual Learning Robot in Repeated InteractionsACM Transactions on Human-Robot Interaction10.1145/365911013:4(1-39)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3659110
Ayub AFendley CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Few-shot continual active learning by a robotProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602489(30612-30624)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602489

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