Title :
Hybrid neurofuzzy online learning for optimal grasping
Author :
Domínguez-López, J.A. ; Damper, R.I. ; Crowder, R.M. ; Harris, C.J.
Author_Institution :
Dept. of Electron. & Comput. Sci., Southampton Univ., UK
Abstract :
In this paper, we describe the application of various machine learning methods to the problem of robust control of a robotic end effector. The methods studied are supervised neurofuzzy learning, unsupervised reinforcement learning and a supervised/unsupervised hybrid. Results show that the hybrid learning is superior in our tests.
Keywords :
end effectors; fuzzy control; neurocontrollers; robust control; unsupervised learning; hybrid neurofuzzy online learning; machine learning; neurofuzzy control; optimal grasping; robotic end effector; robust control; supervised neurofuzzy learning; supervised/unsupervised hybrid learning; unsupervised reinforcement learning; Artificial neural networks; Control systems; End effectors; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Learning; Robots; Shape control;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259588
