Analyzing the Impact of Different Feature Queries in Active Learning for Social Robots
- 60 Downloads
In recent years, the role of social robots is gaining popularity in our society but still learning from humans is a challenging problem that needs to be addressed. This paper presents an experiment where, after teaching poses to a robot, a group of users are asked several questions whose answers are used to create feature filters in the robot’s learning space. We study how the answers to different types of questions affect the learning accuracy of a social robot when it is trained to recognize human poses. We considered three types of questions: “Free Speech Queries”, “Yes/No Queries”, and “Rank Queries”, building a feature filter for each type of question. Besides, we provide another filter to help the robot to reduce the effects of inaccurate answers: the Extended Filter. We compare the performance of a robot that learned the same poses with Active Learning (using the four feature filters) versus Passive Learning (without filters). Our results show that, despite the fact that Active Learning can improve the robot’s learning accuracy, there are some cases where this approach, using the feature filters, achieves significant worse results than Passive Learning if the user provides inaccurate feedback when asked. However, the Extended Filter has proven to maintain the benefits of Active Learning even when the user answers are not accurate.
KeywordsActive learning Social robots Robot learning Pose detection
The research leading to these results has received funding from the ROBSEN jproject (Desarrollo de robots sociales para ayuda a mayores con deterioro cognitivo; DPI2014-57684-R) funded by Spanish Ministry of Economy and Competitiveness and from the RoboCity2030-III-CM project (Robtica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Alonso F, Gorostiza J, Salichs M (2013) Preliminary experiments on HRI for improvement the Robotic Dialog System (RDS). In: Robocity2030 11th Workshop: Robots Sociales. Leganes, SpainGoogle Scholar
- 6.Cakmak M, Thomaz A L (2012) Designing robot learners that ask good questions. In: Proceedings of the seventh annual ACM/IEEE international conference on Human–Robot Interaction—HRI ’12. ACM Press, p 17Google Scholar
- 8.Chao C, Cakmak M, Thomaz AL (2010) Transparent active learning for robots. In: 2010 5th ACM/IEEE international conference on Human–Robot Interaction (HRI). IEEE, pp 317–324Google Scholar
- 13.Quigley M, Gerkey B, Conley K, Faust J, Foote T, Leibs J, Berger E, Wheeler R, Ng A (2009) ROS: an open-source Robot Operating System. In: Open-Source Software workshop of the international conference on robotics and automation (ICRA)Google Scholar
- 14.Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann series in machine learning. Morgan Kaufmann PublishersGoogle Scholar
- 15.Rosenthal S, Dey AK, Veloso M (2009) How robots’ questions affect the accuracy of the human responses. In: RO-MAN 2009—The 18th IEEE international symposium on robot and human interactive communication. IEEE, pp 1137–1142Google Scholar
- 17.Salichs M, Barber R, Khamis A, Malfaz M, Gorostiza J, Pacheco R, Rivas R, Corrales A, Delgado E, Garcia D (2006) Maggie: a robotic platform for human-robot social interaction. In: 2006 IEEE Conference on Robotics. Automation and Mechatronics. IEEE, Bangkok, pp 1–7Google Scholar
- 18.Settles B (2010) Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-MadisonGoogle Scholar
- 20.Thomaz A, Breazeal C (2007) Robot learning via socially guided exploration. In: IEEE 6th international conference on development and learning, 2007. ICDL 2007. IEEE, pp 82–87Google Scholar
- 21.Thomaz A, Hoffman G, Breazeal C (2006) Reinforcement learning with human teachers: understanding how people want to teach robots. In: ROMAN 2006—the 15th IEEE international symposium on robot and human interactive communication. IEEE, pp 352–357Google Scholar