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
A dynamical system approach to realtime obstacle avoidance
Autonomous Robots
Estimating the non-linear dynamics of free-flying objects
Robotics and Autonomous Systems
From dynamic movement primitives to associative skill memories
Robotics and Autonomous Systems
Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning
Robotics and Autonomous Systems
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
Encoding bi-manual coordination patterns from human demonstrations
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing
International Journal of Automation and Computing
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This paper presents a method to learn discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e., time-invariant) dynamical system (DS) and define sufficient conditions to ensure global asymptotic stability at the target. We propose a learning method, which is called Stable Estimator of Dynamical Systems (SEDS), to learn the parameters of the DS to ensure that all motions closely follow the demonstrations while ultimately reaching and stopping at the target. Time-invariance and global asymptotic stability at the target ensures that the system can respond immediately and appropriately to perturbations that are encountered during the motion. The method is evaluated through a set of robot experiments and on a library of human handwriting motions.