Transferring human skills to dextrous robots in an easy, fast and robust way is one of the key challenges that still have to be tackled in order to bring robots to our every-day life. However, many problems remain uns...
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ISBN:
(纸本)9781424466757
Transferring human skills to dextrous robots in an easy, fast and robust way is one of the key challenges that still have to be tackled in order to bring robots to our every-day life. However, many problems remain unsolved. In particular, researchers are seeking new paradigms along with efficient and robust task representations that facilitate adaptation to new contexts and provide a means to appropriately react to unforeseen situations. In this paper, we present a new method for robot behaviour synthesis, where intrinsic characteristic of 'Structured UKR manifolds' [13] are used to derive a closed-loop controller based on motion data obtained by the 'Robot Skill Synthesis via Human Learning' paradigm [10]. We apply the method to the task of swapping Chinese health balls with a real 16 DOF robotic hand. Our results indicate that the marriage of 'Structured UKR manifolds' with the 'Robot Skill Synthesis via Human Learning' paradigm yields an efficient way of realising a dextrous manipulation capability on real robots.
The Nadaraya-Watson estimator, also known as kernelregression, is a density-based regression technique. It weights output values with the relative densities in input space. The density is measured with kernel functio...
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The Nadaraya-Watson estimator, also known as kernelregression, is a density-based regression technique. It weights output values with the relative densities in input space. The density is measured with kernel functions that depend on bandwidth parameters. In this work we present an evolutionary bandwidth optimizer for kernelregression. The approach is based on a robust loss function, leave-one-out cross-validation, and the CMSA-ES as optimization engine. A variant with local parameterized Nadaraya-Watson models enhances the approach, and allows the adaptation of the model to local data space characteristics. The unsupervised counterpart of kernelregression is an approach to learn principal manifolds. The learning problem of unsupervised kernel regression (UKR) is based on optimizing the latent variables, which is a multimodal problem with many local optima. We propose an evolutionary framework for optimization of UKR based on scaling of initial local linear embedding solutions, and minimization of the cross-validation error. Both methods are analyzed experimentally. (C) 2012 Elsevier Ltd. All rights reserved.
We present a generic approach to integrate feature maps with a competitive layer architecture to enable segmentation by a competitive neural dynamics specified in terms of the latent space mappings constructed by the ...
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We present a generic approach to integrate feature maps with a competitive layer architecture to enable segmentation by a competitive neural dynamics specified in terms of the latent space mappings constructed by the feature maps. We demonstrate the underlying ideas for the case of motion segmentation, using a system that employs unsupervised kernel regression (UKR) for the creation of the feature maps, and the Competitive Layer Model (CLM) for the competitive layer architecture. The UKR feature maps hold learned representations of a set of candidate motions and the CLM dynamics, working on features defined in the UKR domain, implements the segmentation of observed trajectory data according to the competing candidates. We also demonstrate how the introduction of an additional layer can provide the system with a parametrizable rejection mechanism for previously unknown observations. The evaluation on trajectories describing four different letters yields improved classification results compared to our previous, pure manifold approach. (C) 2011 Elsevier B.V. All rights reserved.
Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the cor...
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ISBN:
(纸本)9781424441990
Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the correct inner structure of data sets. In this paper, we proposed a new locality-based manifold learning method - Neighborhood Balance Embedding. The proposed method share the same 'neighborhood preserving' property with other manifold learning methods, however, it describe the local structure in a different way, which makes each neighborhood like as rigid balls, thus prevents the overlapping phenomenon which often happens when coping with highly nonlinear data. Experimental results on the data sets with high nonlinearity show good performances of the proposed method.
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