- April 2015
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- Active robot learners take an active role in their own learning by making queries to their human teachers when they receive new data. However, not every received input is useful for the robot, and asking for non-informative inputs or asking too many questions might worsen the user's perception of the robot. We present a novelty detection system that enables a robot to ask labels for new stimuli only when they seem both novel and interesting. Our system separates the decision process into two steps: first, it discriminates novel from known stimuli, and second, it estimates if these stimuli are likely to happen again. Our approach uses the notion of curiosity, which controls the eagerness with which the robot asks questions to the user. We evaluate our approach in the domain of pose learning by training our robot with a set of pointing poses able to detect up to 84%, 79%, and 78% of the observed novelties in three different experiments. Our approach enables robots to keep learning continuously, even after training is finished. The introduction of the curiosity parameter allows tuning, for the conditions in which the robot should want to learn more.
- novelty detection; interactive learning; cumulative learning