Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
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ECML '07 Proceedings of the 18th European conference on Machine Learning
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Intelligent Service Robotics
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Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Programming new skills on a robot should take minimal time and effort. One approach to achieve this goal is to allow the robot to ask questions. This idea, called Active Learning, has recently caught a lot of attention in the robotics community. However, it has not been explored from a human-robot interaction perspective. In this paper, we identify three types of questions (label, demonstration and feature queries) and discuss how a robot can use these while learning new skills. Then, we present an experiment on human question asking which characterizes the extent to which humans use these question types. Finally, we evaluate the three question types within a human-robot teaching interaction. We investigate the ease with which different types of questions are answered and whether or not there is a general preference of one type of question over another. Based on our findings from both experiments we provide guidelines for designing question asking behaviors on a robot learner.