Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
A tennis serve and upswing learning robot based on bi-directional theory
Neural Networks - Special issue on neural control and robotics: biology and technology
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning from observation using primitives
Learning from observation using primitives
Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning in behavior-based multi-robot systems: policies, models, and other agents
Cognitive Systems Research
Multi-thresholded approach to demonstration selection for interactive robot learning
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Teaching a robot to perform task through imitation and on-line feedback
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Using informative behavior to increase engagement in the tamer framework
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by interacting with a teacher. In this work we present an algorithm for learning by demonstration in which the teacher operates in two phases. The teacher first demonstrates the task to the learner. The teacher next critiques learner performance of the task. This critique is used by the learner to update its control policy. In our implementation we utilize a 1-Nearest Neighbor technique which incorporates both training dataset and teacher critique. Since the teacher critiques performance only, they do not need to guess at an effective critique for the underlying algorithm. We argue that this method is particularly well-suited to human teachers, who are generally better at assigning credit to performances than to algorithms. We have applied this algorithm to the simulated task of a robot intercepting a ball. Our results demonstrate improved performance with teacher critiquing, where performance is measured by both execution success and efficiency.