Title :
Direct Mapping of Visual Input to Motor Torques
Author :
Neubert, Jeremiah J. ; Ferrier, Nicola J.
Author_Institution :
Dept. of Eng., Cambridge Univ.
Abstract :
Most methods for visual control of robots formulate the robot command in joint or Cartesian space. To move the robot these commands are remapped to motor torques usually requiring a dynamic model of the robot. In this paper we present a method for parameterizing joint torques and learning to map visual input directly to them. The system is implemented and used to control a CRS 465 robot. The results of the implementation demonstrate that the parameterization of the torques allows both the motion and position of the robot´s end effectors to be controlled. Moreover, it is shown that it is possible to map visual input directly to joint torques
Keywords :
end effectors; learning (artificial intelligence); manipulator dynamics; manipulator kinematics; motion control; neurocontrollers; position control; robot vision; torque control; CRS 465 robot; Cartesian space; direct visual input mapping; joint torque parameterization; learning; motion control; motor torques; position control; robot command; robot dynamic model; robot end effectors; robot visual control; Control systems; Data mining; End effectors; Motion control; Orbital robotics; Robot control; Robot motion; Shape control; Table lookup; Torque control;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.452
