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Adapting Preshaped Grasping Movements Using Vision Descriptors

Oliver Krömer; Renaud Detry; Justus H. Piater; Jan Peters
In: Stéphane Doncieux; Benoît Girard; Agnès Guillot; John Hallam; Jean-Arcady Meyer; Jean-Baptiste Mouret (Hrsg.). From Animals to Animats 11, 11th International Conference on Simulation of Adaptive Behavior, SAB 2010. Proceedings. International Conference on Simulation of Adaptive Behavior (SAB-2010), August 25-28, Paris, France, Pages 156-166, Lecture Notes in Computer Science, Vol. 6226, Springer, 2010.


Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot’s learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object’s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.

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