|Title: A visually guided robot and a neural network join to grasp slanted objects Proceedings of the 3rd SNN Symposium on Neural Networks|
|Written by: Smagt P van der, Dev A, Groen F|
|in: Sep. 1995|
|on pages: 144-150|
|Editor: B. Kappen and S. Gielen|
|Publisher: Foundation for Neural Networks, Nijmegen|
|Address: Nijmegen, Netherlands|
Abstract: In this paper we introduce a method for model-free monocular visual guidance of a robot arm. The robot arm, with a single camera in its end-effector, should be positioned above a target, with a changing pan and tilt, which is placed against a textured background. It is shown that a trajectory can be planned in visual space by using components of the optic flow, and this trajectory can be translated to joint torques by a self-learning neural network. No model of the robot, camera, or environment is used. The method reaches a high grasping accuracy after only a few trials.