COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Managing autonomy in robot teams: observations from four experiments
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Learning by demonstration with critique from a human teacher
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
Learning and interacting in human-robot domains
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning about objects with human teachers
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
The oz of wizard: simulating the human for interaction research
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Bayesian network-based behavior control for Skilligent robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An effective personal mobile robot agent through symbiotic human-robot interaction
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A Human-Robot Collaborative Reinforcement Learning Algorithm
Journal of Intelligent and Robotic Systems
Teacher feedback to scaffold and refine demonstrated motion primitives on a mobile robot
Robotics and Autonomous Systems
Designing robot learners that ask good questions
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Safe exploration of state and action spaces in reinforcement learning
Journal of Artificial Intelligence Research
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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Effective learning from demonstration techniques enable complex robot behaviors to be taught from a small number of demonstrations. A number of recent works have explored interactive approaches to demonstration, in which both the robot and the teacher are able to select training examples. In this paper, we focus on a demonstration selection algorithm used by the robot to identify informative states for demonstration. Existing automated approaches for demonstration selection typically rely on a single threshold value, which is applied to a measure of action confidence. We highlight the limitations of using a single fixed threshold for a specific subset of algorithms, and contribute a method for automatically setting multiple confidence thresholds designed to target domain states with the greatest uncertainty. We present a comparison of our multi-threshold selection method to confidence-based selection using a single fixed threshold, and to manual data selection by a human teacher. Our results indicate that the automated multi-threshold approach significantly reduces the number of demonstrations required to learn the task.