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.
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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.
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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.
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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
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