A Human-Robot Collaborative Reinforcement Learning Algorithm

Authors:
Uri Kartoun;Helman Stern;Yael Edan
Affiliations:
Microsoft Medical Medial Lab (M3L), Microsoft, Washington, USA;Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel 84105;Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel 84105
Venue:
Journal of Intelligent and Robotic Systems
Year:
2010

Citing 26
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Incremental multi-step Q-learning

Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain

Autonomous Robots
Embedding a Priori Knowledge in Reinforcement Learning

Journal of Intelligent and Robotic Systems
Reinforcement Learning Agents

Artificial Intelligence Review
Robot Learning From Demonstration

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Practical Reinforcement Learning in Continuous Spaces

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Introspection Approach to Querying a Trainer

An Introspection Approach to Querying a Trainer
Apprenticeship learning via inverse reinforcement learning

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic preferences in multi-criteria reinforcement learning

ICML '05 Proceedings of the 22nd international conference on Machine learning
A Reinforcement Learning Algorithm in Cooperative Multi-Robot Domains

Journal of Intelligent and Robotic Systems
Knotting/Unknotting Manipulation of Deformable Linear Objects

International Journal of Robotics Research
Hybrid Dynamic Control Algorithm for Humanoid Robots Based on Reinforcement Learning

Journal of Intelligent and Robotic Systems
Confidence-based policy learning from demonstration using Gaussian mixture models

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Learning to Control in Operational Space

International Journal of Robotics Research
Teachable robots: Understanding human teaching behavior to build more effective robot learners

Artificial Intelligence
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
An Adaptable Oscillator-Based Controller for Autonomous Robots

Journal of Intelligent and Robotic Systems
Multi-robot task allocation through vacancy chain scheduling

Robotics and Autonomous Systems
Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Accelerating reinforcement learning through implicit imitation

Journal of Artificial Intelligence Research
Interactive policy learning through confidence-based autonomy

Journal of Artificial Intelligence Research
Representation for knot-tying tasks

IEEE Transactions on Robotics
A new Q-learning algorithm based on the metropolis criterion

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new reinforcement learning algorithm that enables collaborative learning between a robot and a human. The algorithm which is based on the Q(驴) approach expedites the learning process by taking advantage of human intelligence and expertise. The algorithm denoted as CQ(驴) provides the robot with self awareness to adaptively switch its collaboration level from autonomous (self performing, the robot decides which actions to take, according to its learning function) to semi-autonomous (a human advisor guides the robot and the robot combines this knowledge into its learning function). This awareness is represented by a self test of its learning performance. The approach of variable autonomy is demonstrated and evaluated using a fixed-arm robot for finding the optimal shaking policy to empty the contents of a plastic bag. A comparison between the CQ(驴) and the traditional Q(驴)-reinforcement learning algorithm, resulted in faster convergence for the CQ(驴) collaborative reinforcement learning algorithm.