Requirements and design of a grasping system for personal robots

In this paper, we present a robotic grasping system for deployment in perso nal robots. The system learns how to grasp objects from experiments. This a pproach allows it to satisfy a number of requirements that we have identifi ed as prerequisite for operation in personal robot environments. The system design consists of three control layers, each describing the control strat egy of a predefined behavior. Learning of the behavior ib performed using g roups of neural networks. Testing of the system was performed in a simulate d environment using a specially built grasping simulator and using a 15 obj ects database. Results show that, on average, each object needed 12 success ful experiments before an accurate grasping model was achieved. Failed expe riments averaged to 25% of the total experiments. (C) 1998 Published by Els evier Science Ltd. Ail rights reserved.