Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Convergence of Gradient Dynamics with a Variable Learning Rate
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
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
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
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Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the Learning from Demonstration paradigm. Here a policy mapping world observations to action selection is learned, by generalizing from task demonstrations by a teacher. Most Learning from Demonstration work to date considers data from a single teacher. In this paper, we consider the incorporation of demonstrations from multiple teachers. In particular, we contribute an algorithm that handles multiple data sources, and additionally reasons about reliability differences between them. For example, multiple teachers could be inequally proficient at performing the demonstrated task. We introduce Demonstration Weight Learning (DWL) as a Learning from Demonstration algorithm that explicitly represents multiple data sources and learns to select between them, based on their observed reliability and according to an adaptive expert learning inspired approach. We present a first implementation of DWL within a simulated robot domain. Data sources are shown to differ in reliability, and weighting is found impact task execution success. Furthermore, DWL is shown to produce appropriate data source weights that improve policy performance.