Requirements and design of a grasping system for personal robots

In this paper, we present a robotic grasping system for deployment in personal robots. The system learns how to grasp objects from experiments. This approach allows it to satisfy a number of requirements that we have identified as prerequisite for operation in personal robot environments. The system design consists of three control layers, each describing the control strategy of a predefined behavior. Learning of the behavior is performed using groups of neural networks. Testing of the system was performed in a simulated environment using a specially built grasping simulator and using a 15 objects database. Results show that, on average, each object needed 12 successful experiments before an accurate grasping model was achieved. Failed experiments averaged to 25% of the total experiments.