PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adapting preshaped grasping movements using vision descriptors
Oliver Kroemer, Renaud Detry, Justus Piater and Jan Peters
In: International Conference on the Simulation of Adaptive Behavior, 24-28 Aug 2010, France.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8031
Deposited By:Oliver Kroemer
Deposited On:17 March 2011