From egocentric to allocentric spatial behavior: a computational model of spatial development
Adaptive Behavior - Special issue on biologically inspired models of navigation
Three sources of information in social learning
Imitation in animals and artifacts
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
ICML '05 Proceedings of the 22nd international conference on Machine learning
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Trajectory prediction: learning to map situations to robot trajectories
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robot trajectory optimization using approximate inference
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Automatic selection of task spaces for imitation learning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
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Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a new situation. In effect, extensive data is accumulated containing situations together with the respective optimized trajectories--but this data is in practice hardly exploited. This article describes a novel method to learn from such data and speed up motion generation, a method we denote tajectory pediction. The main idea is to use demonstrated optimal motions to quickly predict appropriate trajectories for novel situations. These can be used to initialize and thereby drastically speed-up subsequent optimization of robotic movements. Our approach has two essential ingredients. First, to generalize from previous situations to new ones we need a situation descriptor--we construct features for such descriptors and use a sparse regularized feature selection approach to improve generalization. Second, the transfer of previously optimized trajectories to a new situation should not be made in joint angle space--we propose a more efficient task space transfer. We present extensive results in simulation to illustrate the benefits of the new method, and demonstrate it also with real robot hardware. Our experiments in diverse tasks show that we can predict good motion trajectories in new situations for which the refinement is much faster than an optimization from scratch.