Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning procedural knowledge through observation
Proceedings of the 1st international conference on Knowledge capture
Machine Learning
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
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Teaching and Working with Robots as a Collaboration
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Acquiring a robust case base for the robot soccer domain
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning and interacting in human-robot domains
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multi-thresholded approach to demonstration selection for interactive robot learning
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Teaching multi-robot coordination using demonstration of communication and state sharing
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Connection Science - Social Learning in Embodied Agents
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Relational Learning by Imitation
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Automatic weight learning for multiple data sources when learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Transparent active learning for robots
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Learning multirobot joint action plans from simultaneous task execution demonstrations
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
Exploiting social partners in robot learning
Autonomous Robots
Demonstration based Policy Learning in a Reduced Driving Environment
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Human and robot perception in large-scale learning from demonstration
Proceedings of the 6th international conference on Human-robot interaction
Robot learning from demonstration by constructing skill trees
International Journal of Robotics Research
Safe exploration of state and action spaces in reinforcement learning
Journal of Artificial Intelligence Research
Prediction from expert demonstrations for safe tele-surgery
International Journal of Automation and Computing
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as a set of Gaussian mixture models (GMMs), where each model, with multiple Gaussian components, corresponds to a single action. Incrementally received demonstration examples are used as training data for the GMM set. We then introduce our confident execution approach, which focuses learning on relevant parts of the domain by enabling the agent to identify the need for and request demonstrations for specific parts of the state space. The agent selects between demonstration and autonomous execution based on statistical analysis of the uncertainty of the learned Gaussian mixture set. As it achieves proficiency at its task and gains confidence in its actions, the agent operates with increasing autonomy, eliminating the need for unnecessary demonstrations of already acquired behavior, and reducing both the training time and the demonstration workload of the expert. We validate our approach with experiments in simulated and real robot domains.