Title :
Learning probabilistic models for optimal visual servo control of dynamic manipulation
Author :
Nikovski, Daniel ; Nourbakhsh, Illah
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.
Keywords :
Markov processes; manipulators; optimal control; servomechanisms; state-space methods; Markov decision process; dynamic manipulation; grounding states; optimal control; robots; sequential visual servo control; servo control; Adaptive control; Control systems; Cost function; Kinematics; Manipulator dynamics; Motion control; Optimal control; Robot sensing systems; Servomechanisms; Servosystems;
Conference_Titel :
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7398-7
DOI :
10.1109/IRDS.2002.1041533
