Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Adapting preshaped grasping movements using vision descriptors
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Policy search for motor primitives in robotics
Machine Learning
Interactive imitation learning of object movement skills
Autonomous Robots
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
Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
IEEE Transactions on Robotics
IEEE Transactions on Robotics
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Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn to cooperate. Simulations as well as real-robot experiments are shown. Interestingly, all these mechanisms are entirely based on low level interactions without any planning or cognitive component.