Adapting Preshaped Grasping Movements Using Vision Descriptors

  • Oliver Krömer
  • Renaud Detry
  • Justus Piater
  • Jan Peters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6226)


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.


Obstacle Avoidance Humanoid Robot Stereo Vision Real Robot Canonical System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oliver Krömer
    • 1
  • Renaud Detry
    • 1
  • Justus Piater
    • 1
  • Jan Peters
    • 1
  1. 1.Max Planck Inistitute for Biological CyberneticsTübignenGermany

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