Principles of multivariate analysis: a user's perspective
Principles of multivariate analysis: a user's perspective
A teaching method for reinforcement learning
ML92 Proceedings of the ninth international workshop on Machine learning
Mathematical Programming: Series A and B
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Imitation in animals and artifacts
Imitation in animals and artifacts
Three sources of information in social learning
Imitation in animals and artifacts
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Socially guided machine learning
Socially guided machine learning
Connection Science - Social Learning in Embodied Agents
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Active learning with statistical models
Journal of Artificial Intelligence Research
A Computational Model of Social-Learning Mechanisms
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robot Programming by Demonstration
Robot Programming by Demonstration
Teacher feedback to scaffold and refine demonstrated motion primitives on a mobile robot
Robotics and Autonomous Systems
Policy search for motor primitives in robotics
Machine Learning
Trajectories and keyframes for kinesthetic teaching: a human-robot interaction perspective
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)
IEEE Transactions on Autonomous Mental Development
Goal Babbling Permits Direct Learning of Inverse Kinematics
IEEE Transactions on Autonomous Mental Development
Designing Interactions for Robot Active Learners
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Learning the combinatorial structure of demonstrated behaviors with inverse feedback control
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
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This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our algorithmic architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line, we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.