Abstract
This paper assesses how the accuracy in user’s answers influence the learning of a social robot when it is trained to recognize poses using Active Learning. We study the performance of a robot trained to recognize the same poses actively and passively and we show that, sometimes, the user might give simplistic answers producing a negative impact on the robot’s learning. To reduce this effect, we provide a method based on lowering the trust in the user’s responses. We conduct experiments with 24 users, indicating that our method maintains the benefits of AL even when the user answers are not accurate. With this method the robot incorporates domain knowledge from the users, mitigating the impact of low quality answers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Questions and queries will be used indistinctly in this paper.
- 2.
- 3.
Note that we use the terms relevance and importance indistinctly.
- 4.
The original questions were asked in Spanish. Here we provide the most accurate translations we have found.
References
Alonso, F., Gorostiza, J., Salichs, M.: Preliminary experiments on HRI for improvement the Robotic Dialog System (RDS). In: Robocity2030 11th Workshop on Social Robots (2013)
Alonso-Martín, F., Salichs, M.A.: Integration of a voice recognition system in a socia robot. Cybern. Syst. 42(4), 215–245 (2011)
Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cakmak, M., Chao, C., Thomaz, A.L.: Designing interactions for robot active learners. IEEE Trans. Auton. Ment. Dev. 2(2), 108–118 (2010)
Cakmak, M., Thomaz, A.L.: Designing robot learners that ask good questions. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2012, p. 17. ACM, New York (2012)
Druck, G., Settles, B., McCallum, A.: Active learning by labeling features. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 81–90. Association for Computational Linguistics (2009)
Gonzalez-Pacheco, V., Malfaz, M., Fernandez, F., Salichs, M.A.: Teaching human poses interactively to a social robot. Sens. 13(9), 12406–12430 (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Lopes, M., Oudeyer, P.Y.: Guest editorial active learning and intrinsically motivated exploration in robots: advances and challenges. IEEE Trans. Auton. Ment. Dev. 2(2), 65–69 (2010)
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.: ROS: an open-source Robot Operating System. In: Open-Source SW Workshop of the International Conference on Robotics and Automation (ICRA) (2009)
Raghavan, H., Madani, O., Jones, R.: Active learning with feedback on features and instances. J. Mach. Learn. Res. 7, 1655–1686 (2006)
Rosenthal, S., Dey, A.K., Veloso, M.: How robots’ questions affect the accuracy of the human responses. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2009, pp. 1137–1142. IEEE (2009)
Salichs, M., Barber, R., Khamis, A., Malfaz, M., Gorostiza, J., Pacheco, R., Rivas, R., Corrales, A., Delgado, E., Garcia, D.: Maggie: a robotic platform for human-robot social interaction. In: 2006 IEEE Conference on Robotics. Automation and Mechatronics, pp. 1–7. IEEE, Bangkok, December 2006
Settles, B.: Active learning literature survey. Computer Sciences Technical report 1648, University of Wisconsin-Madison (2010)
Acknowledgment
This research has received funding from the projects Development of social robots to help seniors with cognitive impairment - ROBSEN funded by the Ministerio de Economía y Competitividad (DPI2014-57684-R) from the Spanish Government; and RoboCity2030-III-CM (S2013/ MIT-2748), funded by Programas de Actividades I+D of the Madrid Regional Authority and cofunded by Structural Funds of the EU.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Gonzalez-Pacheco, V., Malfaz, M., Castillo, J.C., Castro-Gonzalez, A., Alonso-Martín, F., Salichs, M.A. (2016). How Much Should a Robot Trust the User Feedback? Analyzing the Impact of Verbal Answers in Active Learning. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-47437-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47436-6
Online ISBN: 978-3-319-47437-3
eBook Packages: Computer ScienceComputer Science (R0)