A teaching method for reinforcement learning
ML92 Proceedings of the ninth international workshop on Machine learning
Designing Sociable Robots
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Intra-Option Learning about Temporally Abstract Actions
ICML '98 Proceedings of the Fifteenth 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
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Teachable robots: Understanding human teaching behavior to build more effective robot learners
Artificial Intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning about objects with human teachers
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Socially guided intrinsic motivation for robot learning of motor skills
Autonomous Robots
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We present a learning system, socially guided exploration, in which a social robot learns new tasks through a combination of self-exploration and social interaction. The system's motivational drives, along with social scaffolding from a human partner, bias behaviour to create learning opportunities for a hierarchical reinforcement learning mechanism. The robot is able to learn on its own, but can flexibly take advantage of the guidance of a human teacher. We report the results of an experiment that analyses what the robot learns on its own as compared to being taught by human subjects. We also analyse the video of these interactions to understand human teaching behaviour and the social dynamics of the human-teacher/robot-learner system. With respect to learning performance, human guidance results in a task set that is significantly more focused and efficient at the tasks the human was trying to teach, whereas self-exploration results in a more diverse set. Analysis of human teaching behaviour reveals insights of social coupling between the human teacher and robot learner, different teaching styles, strong consistency in the kinds and frequency of scaffolding acts across teachers and nuances in the communicative intent behind positive and negative feedback.