Using Experience for Assessing Grasp Reliability

Manipulation skills are a key issue for a humanoid robot. Here, we are interested in a vision-based grasping system able to deal with previously unknown objects in real time and in an intelligent man- ner. Starting from a number of feasible candidate grasps, we focus on the problem of predicting their reliability using the knowledge acquired in previous grasping experiences. A set of visual features which take into account physical properties that can aect the stability and reliability of a grasp are dened. A humanoid robot obtains its grasping experience by repeating a large number of grasping actions on dieren t objects. An experimental protocol is established in order to classify grasps according to their reliability. Two prediction/classication strategies are dened which allow the robot to predict the outcome of a grasp only analizing its visual features. The results indicate that these strategies are adequate to predict the realibility of a grasp and to generalize to dieren t objects.

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