In this paper, we address the problem of extracting knowledge on the graspability of unknown objects based on visual information using an Early Cognitive Vision system representing contours and surfaces on different level of granularity. We present an approach towards automatically identifying configurations of three 3D surface features that predict grasping actions with a high success probability. The strategy is based on first computing spatial relations between visual entities and secondly, exploring the cross-space of these relational feature space and grasping actions. The data foundation for identifying such indicative feature constellations is generated in a simulated environment wherein visual features are extracted and a large amount of grasping actions are evaluated through dynamic simulation. Based on the identified feature constellations, we validate by applying the acquired knowledge on a set of novel objects and evaluating the proposed grasping actions in dynamic simulation. In this validation, we have achieved an average success-rate between 0.74 and 0.89 when relying on the information inherent in configuration of three 3D surface features.
I E E E International Conference on Robotics and Automation, 2013
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Bootstrapping Structural Knowledge from Sensory-motor Experience Workshop at IEEE International Conference on Robotics and AutomationInternational Conference on Robotics and Automation, 2013