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Grasping with Vision Descriptors and Motor Primitives

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). Grasping with Vision Descriptors and Motor Primitives. In 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010) (pp. 47-54). Lisboa, Portugal: SciTePress.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BF9E-A
Zusammenfassung
Grasping is one of the most important abilities needed for future service robots. Given the task of picking up an object from betweem 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 may not always be available. Therefore, methods for executing grasps are required, which perform well with information gathered from only standard stereo vision, and make only a few necessary assumptions about the task environment. We propose techniques that reactively modify the robot’s learned motor primitives based on information derived from Early Cognitive Vision descriptors. The proposed techniques employ non-parametric potential fields centered on the Early Cognitive Vision descriptors to allow for curving hand trajectories around objects, and finger motions that adapt to the object’s local geometry. The methods were tested on a real robot and found to allow for easier imitation learning of human movements and give a considerable improvement to the robot’s performance in grasping tasks.