Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On integrating apprentice learning and reinforcement learning
On integrating apprentice learning and reinforcement learning
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
A framework for learning from demonstration, generalization and practice in human-robot domains
A framework for learning from demonstration, generalization and practice in human-robot domains
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART 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
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Skill acquisition and use for a dynamically-balancing soccer robot
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
MARIOnET: motion acquisition for robots through iterative online evaluative training
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
Learning from demonstration using MDP induced metrics
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Policy transformation for learning from demonstration
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
A practical comparison of three robot learning from demonstration algorithms
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Toward learning perceptually grounded word meanings from unaligned parallel data
SIAC '12 Proceedings of the Second Workshop on Semantic Interpretation in an Actionable Context
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Using informative behavior to increase engagement in the tamer framework
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Socially guided intrinsic motivation for robot learning of motor skills
Autonomous Robots
Hi-index | 0.00 |
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to request a demonstration from the human teacher and to learn a policy based on the acquired data. The algorithm selects demonstrations based on a measure of action selection confidence, and our results show that using Confident Execution the agent requires fewer demonstrations to learn the policy than when demonstrations are selected by a human teacher. The second algorithmic component, Corrective Demonstration, enables the teacher to correct any mistakes made by the agent through additional demonstrations in order to improve the policy and future task performance. CBA and its individual components are compared and evaluated in a complex simulated driving domain. The complete CBA algorithm results in the best overall learning performance, successfully reproducing the behavior of the teacher while balancing the tradeoff between number of demonstrations and number of incorrect actions during learning.