Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Operational Space Control: A Theoretical and Empirical Comparison
International Journal of Robotics Research
Generality and legibility in mobile manipulation
Autonomous Robots
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators
International Journal of Robotics Research
A dynamical system approach to realtime obstacle avoidance
Autonomous Robots
Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives
Robotics and Autonomous Systems
A Novel Trajectory Generation Method for Robot Control
Journal of Intelligent and Robotic Systems
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
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
Robotics and Autonomous Systems
Probabilistic model-based imitation learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
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Dynamical systems can generate movement trajectories that are robust against perturbations. This article presents an improved modification of the original dynamic movement primitive (DMP) framework by Ijspeert et al [1], [2]. The new equations can generalize movements to new targets without singularities and large accelerations. Furthermore, the new equations can represent a movement in 3D task space without depending on the choice of coordinate system (invariance under invertible affine transformations). Our modified DMP is motivated from biological data (spinal-cord stimulation in frogs) and human behavioral experiments. We further extend the formalism to obstacle avoidance by exploiting the robustness against perturbations: an additional term is added to the differential equations to make the robot steer around an obstacle. This additional term empirically describes human obstacle avoidance. We demonstrate the feasibility of our approach using the Sarcos Slave robot arm: after learning a single placing movement, the robot placed a cup between two arbitrarily given positions and avoided approaching obstacles.