How Much Should a Robot Trust the User Feedback? Analyzing the Impact of Verbal Answers in Active Learning

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.

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  Fernando Fernández,et al.  Teaching Human Poses Interactively to a Social Robot , 2013, Sensors.

[3]  Andrew McCallum,et al.  Active Learning by Labeling Features , 2009, EMNLP.

[4]  Hema Raghavan,et al.  Active Learning with Feedback on Features and Instances , 2006, J. Mach. Learn. Res..

[5]  Maya Cakmak,et al.  Designing Interactions for Robot Active Learners , 2010, IEEE Transactions on Autonomous Mental Development.

[6]  R. Barber,et al.  Maggie: A Robotic Platform for Human-Robot Social Interaction , 2006, 2006 IEEE Conference on Robotics, Automation and Mechatronics.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[9]  Maya Cakmak,et al.  Designing robot learners that ask good questions , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[10]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[11]  Stephanie Rosenthal,et al.  How robots' questions affect the accuracy of the human responses , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[12]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[13]  Pierre-Yves Oudeyer,et al.  Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges , 2010, IEEE Trans. Auton. Ment. Dev..

[14]  Fernando Alonso-Martín,et al.  INTEGRATION OF A VOICE RECOGNITION SYSTEM IN A SOCIAL ROBOT , 2011, Cybern. Syst..