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Adapting Preshaped Grasping Movements Using Vision Descriptors

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84027

Kroemer,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84139

Detry R, Piater,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Kroemer, O., Detry R, Piater, J., & Peters, J. (2010). Adapting Preshaped Grasping Movements Using Vision Descriptors. From Animals to Animats 11: Eleventh International Conference on the Simulation of Adaptive Behavior (SAB 2010), 156-166.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BEB2-2
Zusammenfassung
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 robotamp;lsquo;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 objectamp;lsquo;s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.