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M. Salganicoff and G. Metta and A. Oddera and G. Sandini
ABSTRACT
We describe an unsupervised on-line method for learning of manipulative actions that allows a robot to push an object connected to it with a rotational point contact to a desired point in image-space. By observing the results of its actions on the object's orientation in image-space, the system forms a predictive forward empirical model. This acquired model is used on-line for manipulation planning and control as it improves. Rather than explicitly inverting the forward model to achieve trajectory control, a stochastic action selection technique is used to select the most informative and promising actions, thereby integrating active perception and learning by combining on-line improvement, task directed exploration, and model exploitation. Simulation and experimental results of the approach are presented 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
A Vision-Based Learning Method for Pushing ManipulationSite generated on Friday, 06 January 2006