Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Neural control of rhythmic arm movements
Neural Networks - Special issue on neural control and robotics: biology and technology
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Constructive Incremental Learning from Only Local Information
Neural Computation
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
A Generalized Path Integral Control Approach to Reinforcement Learning
The Journal of Machine Learning Research
Learning variable impedance control
International Journal of Robotics Research
Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
IEEE Transactions on Robotics
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
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In recent years, research on movement primitives has gained increasing popularity. The original goals of movement primitives are based on the desire to have a sufficiently rich and abstract representation for movement generation, which allows for efficient teaching, trial-and-error learning, and generalization of motor skills (Schaal 1999). Thus, motor skills in robots should be acquired in a natural dialog with humans, e.g., by imitation learning and shaping, while skill refinement and generalization should be accomplished autonomously by the robot. Such a scenario resembles the way we teach children and connects to the bigger question of how the human brain accomplishes skill learning. In this paper, we review how a particular computational approach to movement primitives, called dynamic movement primitives, can contribute to learning motor skills. We will address imitation learning, generalization, trial-and-error learning by reinforcement learning, movement recognition, and control based on movement primitives. But we also want to go beyond the standard goals of movement primitives. The stereotypical movement generation with movement primitives entails predicting of sensory events in the environment. Indeed, all the sensory events associated with a movement primitive form an associative skill memory that has the potential of forming a most powerful representation of a complete motor skill.