Grasping with flexible viewing-direction with a learned coordinate transformation network

Cornelius Weber , Konstantinos Karantzis , Stefan Wermter
5th IEEE-RAS International Conference on Humanoid Robots, pages 253--258, doi: 10.1109/ICHR.2005.1573576 - Dec 2005
Associated documents :  
We present a neurally implemented control system where a robot grasps an object while being guided by the visually perceived position of the object. The system consists of three parts operating in a series: (i) A simplified visual system with a what-where pathway localizes the target object in the visual field. (ii) A coordinate transformation network considers the visually perceived object position and the camera pan-tilt angle to compute the target position in a body-centered frame of reference, as needed for motor action. (iii) This body-centered position is then used by a reinforcement-trained network which docks the robot at a table so that it can grasp the object. The novel coordinate transformation network which we describe in detail here allows for a complicated body geometry in which an agent’s sensors such as a camera can be moved with respect to the body, just like the human head and eyes can. The network is trained, allowing a wide range of transformations that need not be implemented by geometrical calculations

 

@InProceedings{WKW05, 
 	 author =  {Weber, Cornelius and Karantzis, Konstantinos and Wermter, Stefan},  
 	 title = {Grasping with flexible viewing-direction with a learned coordinate transformation network}, 
 	 booktitle = {5th IEEE-RAS International Conference on Humanoid Robots},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {253--258},
 	 year = {2005},
 	 month = {Dec},
 	 publisher = {IEEE},
 	 doi = {10.1109/ICHR.2005.1573576}, 
 }