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In Programming by Demonstration, abstract manipulation knowledge has to be learned, that can be used by an autonomous robot system in different environments with arbitrary obstacles. In this work, manipulation strategies are learned by observation of a human teacher and represented as a flexible, constraint-based representation of the search space for motion planning. The learned manipulation strategy...
In this paper we propose a process which is able to generate abstract service robot mission representations, utilized during execution for autonomous, probabilistic decision making, by observing human demonstrations. The observation process is based on the same perceptive components as used by the robot during execution, recording dialog between humans, human motion as well as objects poses. This...
In Programming by Demonstration, a flexible representation of manipulation motions is necessary to learn and generalize from human demonstrations. In contrast to subsymbolic representations of trajectories, e.g. based on a Gaussian Mixture Model, a partially symbolic representation of manipulation strategies based on a temporal satisfaction problem with domain constraints is developed. By using constrained...
The planning of grasping motions is demanding due to the complexity of modern robot systems. In Programming by Demonstration, the observation of a human teacher allows to draw additional information about grasping strategies. Rosell showed, that the motion planning problem can be simplified by globally restricting the set of valid configurations to a learned subspace. In this work, the transformation...
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