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We present an approach that uses combinatorial optimization to decide which spatial relations between objects are relevant to accurately describe an indoor scene, made up of objects. We extract scene models from object configurations that are acquired during demonstration of actions, characteristic for a certain scene. We model scenes as graphs with Implicit Shape Models (ISMs), a Generalized Hough...
We present an approach that combines passive scene understanding with object search in order to recognize scenes in indoor environments that cannot be perceived from a single point of view. Passive scene recognition is performed using Implicit Shape Models based on spatial relations between objects. ISMs, a variant of the Generalized Hough Transform, are extended to describe scenes as sets of objects...
In Programming by Demonstration (PbD), one of the key problems for autonomous learning is to automatically extract the relevant features of a manipulation task, which has a significant impact on the generalization capabilities. In this paper, task features are encoded as constraints of a learned planning model. In order to extract the relevant constraints, the human teacher demonstrates a set of tests,...
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 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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