Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Networks - 2004 Special issue: New developments in self-organizing systems
MESO: Supporting Online Decision Making in Autonomic Computing Systems
IEEE Transactions on Knowledge and Data Engineering
The power of suggestion: teaching sequences through assistive robot motions
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Interactive Demonstration of Pointing Gestures for Virtual Trainers
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
A Probabilistic Model of Motor Resonance for Embodied Gesture Perception
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Learning Relational Grammars from Sequences of Actions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Fuzzy clustering of human motor motion
Applied Soft Computing
Imitation learning and response facilitation in embodied agents
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Humanoid robot behavior learning based on ART neural network and cross-modality learning
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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This work presents a control and learning architecturefor humanoid robots designed for acquiring movementskills in the context of imitation learning.Multiple levels ofmovement abstraction occur across the hierarchical structureof the architecture, finally leading to the representationof movement sequences within a probabilistic framework.As its substrate, the framework uses the notion of visuo-motor primitives, modules capable of recognizing as well as executing similar movements.This notion is heavily motivated by the neuroscience evidence for motor primitives and mirror neurons. Experimental results from an implementation of the architecture are presented involving learning and representation of demonstrated movement sequences from synthetic as well as real human movement data.