research-article

Teaching Human Teachers to Teach Robot Learners

Authors:

Matthew J. HowardAuthors Info & Claims

2018 IEEE International Conference on Robotics and Automation (ICRA)

Pages 1 - 7

https://doi.org/10.1109/ICRA.2018.8461194

Published: 21 May 2018 Publication History

Abstract

Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learner's model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with <tex>$\mathbf{n}=\mathbf{30}$</tex> participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant <tex>$(\mathbf{F}(\mathbf{2},\mathbf{58})=\mathbf{7.952},\mathbf{p}=\mathbf{0.001})$</tex> improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.

References

[1]

A. Billard, S. Calinon, R. Dillmann, and S. Schaal, “Robot Programming by Demonstration,” in Springer Handbook of Robotics. Berlin, Heidelberg: Springer, 2008, pp. 1371–1394.

[2]

S. Calinon and A. Billard, “What is the Teacher's Role in Robot Programming by Demonstration? - Toward Benchmarks for Improved Learning,” Interaction Studies. Special Issue on Psychological Benchmarks in Human-Robot Interaction, vol. 8, no. 3, 2007.

[3]

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469–483, 2009.

Digital Library

[4]

Rethink Robotics, “Intera 5 Online User Guide,” 2017. [Online]. Available: http://mfg.rethinkrobotics.com/intera/Main_Page[Accessed:2017–09-01].

[5]

Universal Robots, “Polyscope User Guide,” 2017. [On-line]. Available: https://www.universal-robots.com/download/?option=27420[Accessed:2017–09–01].

[6]

S. Schaal, “Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics,” in Adaptive Motion of Animals and Machines. Tokyo: Springer-Verlag, 2006, pp. 261–280.

[7]

S. Calinon, F. Guenter, and A. Billard, “On Learning, Representing, and Generalizing a Task in a Humanoid Robot,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, pp. 286–298, 4 2007.

Digital Library

[8]

G. Maeda, M. Ewerton, T. Osa, B. Busch, and J. Peters, “Active Incremental Learning of Robot Movement Primitives,” in Conference on Robot Learning. PMLR, 2017. [Online]. Available: http://proceedings.mlr.press/v78/maeda17a/maeda17a.pdf.

[9]

S. Calinon and A. Billard, “Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM,” in International Conference on Machine Learning. New York, USA: ACM Press, 2005, pp. 105–112.

[10]

R. Toris, H. B. Suay, and S. Chernova, “A practical comparison of three robot learning from demonstration algorithms,” in International Conference on Human-Robot Interaction. New York, USA: ACM Press, 2012, p. 261.

[11]

S. Alexandrova, M. Cakmak, K. Hsiao, and L. Takayama, “Robot Programming by Demonstration with Interactive Action Visualizations,” in Robotics: Science and Systems X, 7 2014.

[12]

M. Cakmak and A. L. Thomaz, “Active Learning with Mixed Query Types in Learning from Demonstration,” in Proceedings of the ICML Workshop on New Developments in Imitation Learning, 2011.

[13]

S. Calinon, “A tutorial on task-parameterized movement learning and retrieval,” Intelligent Service Robotics, vol. 9, no. 1, pp. 1–29, 9 2015.

[14]

M. Cakmak and A. L. Thomaz, “Eliciting good teaching from humans for machine learners,” Artificial Intelligence, vol. 217, pp. 198–215, 2014.

Digital Library

[15]

A. Pervez and D. Lee, “Learning task-parameterized dynamic movement primitives using mixture of GMMs,” Intelligent Service Robotics, pp. 1–18, 7 2017.

[16]

F. Faul, E. Erdfelder, A. Buchner, and A. Lang, “Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses,” Behavior Research Methods, vol. 41, pp. 1149–1160, 2009. [Online]. Available: http://www.gpower.hhu.de/en.html.

[17]

M. Cakmak and A. L. Thomaz, “Optimality of human teachers for robot learners,” in International Conference on Development and Learning. IEEE, 8 2010, pp. 64–69.

[18]

F. Khan, B. Mutlu, and X. Zhu, “How Do Humans Teach: On Curriculum Learning and Teaching Dimension,” in Neural Information Processing Systems, 2011, pp. 1449–1457.

Cited By

Ma SSun MMa X(2023)Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human TeachersACM Transactions on Computer-Human Interaction10.1145/357181330:5(1-48)Online publication date: 23-Sep-2023
https://dl.acm.org/doi/10.1145/3571813
Ding WAbdel-Basset MHawash HAli A(2022)Explainability of artificial intelligence methods, applications and challengesInformation Sciences: an International Journal10.1016/j.ins.2022.10.013615:C(238-292)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.10.013

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Teaching Human Teachers to Teach Robot Learners
1. Computer systems organization

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2018 IEEE International Conference on Robotics and Automation (ICRA)

May 2018

5954 pages

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Published: 21 May 2018

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Cited By

Ma SSun MMa X(2023)Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human TeachersACM Transactions on Computer-Human Interaction10.1145/357181330:5(1-48)Online publication date: 23-Sep-2023
https://dl.acm.org/doi/10.1145/3571813
Ding WAbdel-Basset MHawash HAli A(2022)Explainability of artificial intelligence methods, applications and challengesInformation Sciences: an International Journal10.1016/j.ins.2022.10.013615:C(238-292)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.10.013

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