State space modeling of time series
State space modeling of time series
Universal approximation using radial-basis-function networks
Neural Computation
Artificial Intelligence Review - Special issue on lazy learning
Dynamically-Stable Motion Planning for Humanoid Robots
Autonomous Robots
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Radial Basis Functions
Synthesizing animations of human manipulation tasks
ACM SIGGRAPH 2004 Papers
Constructive Incremental Learning from Only Local Information
Neural Computation
Learning to Control in Operational Space
International Journal of Robotics Research
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Reaching with multi-referential dynamical systems
Autonomous Robots
International Journal of Robotics Research
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Gaussian process dynamic programming
Neurocomputing
Movement curvature planning through force field internal models
Biological Cybernetics
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Unified motion planning of passing under obstacles with humanoid robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
LQR-trees: Feedback Motion Planning via Sums-of-Squares Verification
International Journal of Robotics Research
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
Evaluation of virtual fixtures for a robot programming by demonstration interface
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
Iterative learning of grasp adaptation through human corrections
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
Motion planning and reactive control on learnt skill manifolds
International Journal of Robotics Research
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Motion imitation requires reproduction of a dynamical signature of a movement, i.e. a robot should be able to encode and reproduce a particular path together with a specific velocity and/or an acceleration profile. Furthermore, a human provides only few demonstrations, which cannot cover all possible contexts in which the robot will need to reproduce the motion autonomously. Therefore, the encoding should be able to efficiently generalize knowledge by generating similar motions in unseen contexts. This work follows a recent trend in programming by demonstration in which the dynamics of the motion is learned. We present an algorithm to estimate multivariate robot motions through a mixture of Gaussians. The strengths of the proposed encoding are three-fold: (i) it allows a generalization of motion to unseen context; (ii) it provides fast on-line replanning of the motion in case of of spatio-temporal perturbations; (iii) it may embed different types of dynamics, governed by different attractors. The generality of the method to estimate arbitrary non-linear motion dynamics is demonstrated by accurately estimating a set of known non-linear dynamical systems. The platform-independency and real-time performance of the method are further validated to learn the non-linear motion dynamics of manipulation tasks with different robotic platforms. We provide an experimental comparison of our approach with a related state-of-the-art method.