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Available from: Jeremy L Wyatt
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ABSTRACT: We are investigating the problem of predicting how objects behave under manipulative actions. In particular, we wish to predict the workpiece motions which will result from simple pushing manipulations by a single robotic fingertip. Such interactions are themselves fundamental components of multi-fingered grasping and other complex interactions. Physics simulators can be used to do this, but they model many kinds of object interactions poorly, being dependent on detailed scene descriptions and parameters, which in practice are often difficult to tune. Additionally, we have previously investigated ways of learning to predict, by employing density estimation techniques to learn, from many example pushes, a probabilistic mapping between applied pushing motions and resulting work-piece motions. In contrast, this paper presents an alternative approach to prediction, which does not rely on learning but infers the likelihood of possible workpiece motions by using the simple physics principle of minimum energy. This approach is advantageous in situations where insufficient prior knowledge is available for training our learned predictors. In such situations, possible strategies include either training learned predictors on unrealistic simulation data, or making use of the simple physics approach which requires no training. We show that the second of these strategies performs significantly better, and approaches the performance of learned predictors are trained on observations of real object motions.
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ABSTRACT: The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today׳s classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem of which a robust Kernel Fisher Discriminant is a successful example. However, for classification, discriminative models are of primary interest, and rather curiously, the very few existing label-robust discriminative classifiers are limited to linear problems.
In this paper, we build on the widely used and successful kernelising technique to introduce a label-noise robust Kernel Logistic Regression classifier. The main difficulty that we need to bypass is how to determine the model complexity parameters when no trusted validation set is available. We propose to adapt the Multiple Kernel Learning approach for this new purpose, together with a Bayesian regularisation scheme. Empirical results on 13 benchmark data sets and two real-world applications demonstrate the success of our approach.
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