Learning control for robotic manipulators with sparse dataLearning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.
Document ID
19880040119
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Morita, Atsushi (Mitsubishi Electric Corp. Product Development Laboratory, Amagasaki, Japan)
Dubowsky, Steven (Mitsubishi Electric Corp. Amagasaki, Japan)
Hootsmans, Norbert A. M. (MIT Cambridge, MA, United States)