A Kendama learning robot based on bi-directional theory
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Artificial Intelligence Review - Special issue on lazy learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Using Humanoid Robots to Study Human Behavior
IEEE Intelligent Systems
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
International Journal of Robotics Research
Learning to Control in Operational Space
International Journal of Robotics Research
International Journal of Robotics Research
From Biologically Realistic Imitation to Robot Teaching Via Human Motor Learning
Neural Information Processing
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Online segmentation and clustering from continuous observation of whole body motions
IEEE Transactions on Robotics
Full-Body Compliant Human–Humanoid Interaction: Balancing in the Presence of Unknown External Forces
IEEE Transactions on Robotics
Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
IEEE Transactions on Robotics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalizing behavior obtained from sparse demonstration
Proceedings of the 6th international conference on Human-robot interaction
Human sensorimotor learning for humanoid robot skill synthesis
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A nonparametric Bayesian approach toward robot learning by demonstration
Robotics and Autonomous Systems
On-line motion synthesis and adaptation using a trajectory database
Robotics and Autonomous Systems
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
Reflexive stability control framework for humanoid robots
Autonomous Robots
Learning to select and generalize striking movements in robot table tennis
International Journal of Robotics Research
A new trajectory deformation algorithm based on affine transformations
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
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Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot learning. To be effective, action learning should not be limited to direct replication of movements obtained during training but must also enable the generation of actions in situations a robot has never encountered before. This paper describes a methodology that enables the generalization of the available sensorimotor knowledge. New actions are synthesized by the application of statistical methods, where the goal and other characteristics of an action are utilized as queries to create a suitable control policy, taking into account the current state of the world. Nonlinear dynamic systems are employed as a motor representation. The proposed approach enables the generation of a wide range of policies without requiring an expert to modify the underlying representations to account for different task-specific features and perceptual feedback. The paper also demonstrates that the proposed methodology can be integrated with an active vision system of a humanoid robot. 3-D vision data are used to provide query points for statistical generalization. While 3-D vision on humanoid robots with complex oculomotor systems is often difficult due to the modeling uncertainties, we show that these uncertainties can be accounted for by the proposed approach.