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2008


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Asymmetries of Time Series under Inverting their Direction

Peters, J.

Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

ei

PDF [BibTex]

2008


PDF [BibTex]


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Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression

Nguyen-Tuong, D., Peters, J.

In LAB-RS 2008, pages: 59-64, (Editors: Stoica, A. , E. Tunstel, T. Huntsberger, T. Arslan, S. Vijayakumar, A. O. El-Rayis), IEEE Computer Society, Los Alamitos, CA, USA, 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, August 2008 (inproceedings)

Abstract
Accurate models of the robot dynamics allow the design of significantly more precise, energy-efficient and more compliant computed torque control for robots. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities such as hydraulic cables, complex friction, or actuator dynamics. In such cases, learning the models from data poses an interesting alternative and estimating the dynamics model using regression techniques becomes an important problem. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. We proposed an approximation to the standard GPR using local Gaussian processes models. Due to reduced computational cost, local Gaussian processes (LGP) is capable for an online learning. Comparisons with other nonparametric regre ssions, e.g. standard GPR, SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and SVR while being sufficiently fast for online learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning an Interest Operator from Human Eye Movements

Kienzle, W.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

ei

[BibTex]

[BibTex]


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A Decoupled Approach to Exemplar-based Unsupervised Learning

Nowozin, S., BakIr, G.

In ICML 2008, pages: 704-711, (Editors: Cohen, W. W., A. McCallum, S. Roweis), ACM Press, New York, NY, USA, 25th International Conference on Machine Learning, July 2008 (inproceedings)

Abstract
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possible prototypes to training exemplars. In particular, this has been done for clustering, vector quantization and mixture model density estimation. In this paper we propose a novel algorithm that is theoretically and practically superior to these convex formulations. This is possible by posing the unsupervised learning problem as a single convex master problem" with non-convex subproblems. We show that for the above learning tasks the subproblems are extremely wellbehaved and can be solved efficiently.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Relating clustering stability to properties of cluster boundaries

Ben-David, S., von Luxburg, U.

In COLT 2008, pages: 379-390, (Editors: Servedio, R. A., T. Zhang), Omnipress, Madison, WI, USA, 21st Annual Conference on Learning Theory, July 2008 (inproceedings)

Abstract
In this paper, we investigate stability-based methods for cluster model selection, in particular to select the number K of clusters. The scenario under consideration is that clustering is performed by minimizing a certain clustering quality function, and that a unique global minimizer exists. On the one hand we show that stability can be upper bounded by certain properties of the optimal clustering, namely by the mass in a small tube around the cluster boundaries. On the other hand, we provide counterexamples which show that a reverse statement is not true in general. Finally, we give some examples and arguments why, from a theoretic point of view, using clustering stability in a high sample setting can be problematic. It can be seen that distribution-free guarantees bounding the difference between the finite sample stability and the “true stability” cannot exist, unless one makes strong assumptions on the underlying distribution.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Compressed Sensing and Bayesian Experimental Design

Seeger, M., Nickisch, H.

In ICML 2008, pages: 912-919, (Editors: Cohen, W. W., A. McCallum, S. Roweis), ACM Press, New York, NY, USA, 25th International Conference on Machine Learning, July 2008 (inproceedings)

Abstract
We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study about linearly measuring natural images, we show that the simple standard heuristic of measuring wavelet coefficients top-down systematically outperforms CS methods using random measurements; the sequential projection optimisation approach of (Ji & Carin, 2007) performs even worse. We also show that our own approximate Bayesian method is able to learn measurement filters on full images efficiently which ouperform the wavelet heuristic. To our knowledge, ours is the first successful attempt at "learning compressed sensing" for images of realistic size. In contrast to common CS methods, our framework is not restricted to sparse signals, but can readily be applied to other notions of signal complexity or noise models. We give concrete ideas how our method can be scaled up to large signal representations.

ei

PDF PDF Web DOI [BibTex]

PDF PDF Web DOI [BibTex]


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Tailoring density estimation via reproducing kernel moment matching

Song, L., Zhang, X., Smola, A., Gretton, A., Schölkopf, B.

In Proceedings of the 25th International Conference onMachine Learning, pages: 992-999, (Editors: WW Cohen and A McCallum and S Roweis), ACM Press, New York, NY, USA, ICML, July 2008 (inproceedings)

Abstract
Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distributions into a reproducing kernel Hilbert space, and performing moment matching in that space. This allows us to tailor density estimators to a function class of interest (i.e., for which we would like to compute expectations). We show our density estimation approach is useful in applications such as message compression in graphical models, and image classification and retrieval.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Injective Hilbert Space Embeddings of Probability Measures

Sriperumbudur, B., Gretton, A., Fukumizu, K., Lanckriet, G., Schölkopf, B.

In Proceedings of the 21st Annual Conference on Learning Theory, pages: 111-122, (Editors: RA Servedio and T Zhang), Omnipress, Madison, WI, USA, 21st Annual Conference on Learning Theory (COLT), July 2008 (inproceedings)

Abstract
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. This embedding represents any probability measure as a mean element in a reproducing kernel Hilbert space (RKHS). The embedding function has been proven to be injective when the reproducing kernel is universal. In this case, the embedding induces a metric on the space of probability distributions defined on compact metric spaces. In the present work, we consider more broadly the problem of specifying characteristic kernels, defined as kernels for which the RKHS embedding of probability measures is injective. In particular, characteristic kernels can include non-universal kernels. We restrict ourselves to translation-invariant kernels on Euclidean space, and define the associated metric on probability measures in terms of the Fourier spectrum of the kernel and characteristic functions of these measures. The support of the kernel spectrum is important in finding whether a kernel is characteristic: in particular, the embedding is injective if and only if the kernel spectrum has the entire domain as its support. Characteristic kernels may nonetheless have difficulty in distinguishing certain distributions on the basis of finite samples, again due to the interaction of the kernel spectrum and the characteristic functions of the measures.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery

Blaschko, M., Gretton, A.

In MLG 2008, pages: 1-3, 6th International Workshop on Mining and Learning with Graphs, July 2008 (inproceedings)

Abstract
In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined cluster structure Y , by solving for the partition matrix, II. We extend this approach here to the case where the cluster structure Y is not fixed, but is a quantity to be optimized; and we use an independence criterion which has been shown to be more sensitive at small sample sizes (the Hilbert-Schmidt Normalized Information Criterion, or HSNIC, Fukumizu et al., 2008). We demonstrate the use of this framework in two scenarios. In the first, we adopt a cluster structure selection approach in which the HSNIC is used to select a structure from several candidates. In the second, we consider the case where we discover structure by directly optimizing Y.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation

Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.

In AAAI 2008, pages: 1351-1356, (Editors: Fox, D. , C. P. Gomes), AAAI Press, Menlo Park, CA, USA, Twenty-Third Conference on Artificial Intelligence, July 2008 (inproceedings)

Abstract
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are usually prohibitively expensive. A common approach is to use importance sampling techniques for compensating for the bias caused by the difference between data-sampling policies and the target policy. However, existing off-policy methods do not often take the variance of value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a variant of cross-validation. We demonstrate the usefulness of the proposed approach through simulations.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Sparse Multiscale Gaussian Process Regression

Walder, C., Kim, K., Schölkopf, B.

In Proceedings of the 25th International Conference on Machine Learning, pages: 1112-1119, (Editors: WW Cohen and A McCallum and S Roweis), ACM Press, New York, NY, USA, ICML, July 2008 (inproceedings)

Abstract
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with arbitrary diagonal covariance matrices (or length scales). For a fixed number of basis functions and any given criteria, this additional flexibility permits approximations no worse and typically better than was previously possible. We perform gradient based optimisation of the marginal likelihood, which costs O(m2n) time where n is the number of data points, and compare the method to various other sparse g.p. methods. Although we focus on g.p. regression, the central idea is applicable to all kernel based algorithms, and we also provide some results for the support vector machine (s.v.m.) and kernel ridge regression (k.r.r.). Our approach outperforms the other methods, particularly for the case of very few basis functions, i.e. a very high sparsity ratio.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Example-Based Learning for Single-Image Super-Resolution

Kim, K., Kwon, Y.

In DAGM 2008, pages: 456-463, (Editors: Rigoll, G. ), Springer, Berlin, Germany, 30th Annual Symposium of the German Association for Pattern Recognition, June 2008 (inproceedings)

Abstract
This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection

Lampert, C., Blaschko, M.

In DAGM 2008, pages: 31-40, (Editors: Rigoll, G. ), Springer, Berlin, Germany, 30th Annual Symposium of the German Association for Pattern Recognition, June 2008, Main Award DAGM 2008 (inproceedings)

Abstract
Most current methods for multi-class object classification and localization work as independent 1-vs-rest classifiers. They decide whether and where an object is visible in an image purely on a per-class basis. Joint learning of more than one object class would generally be preferable, since this would allow the use of contextual information such as co-occurrence between classes. However, this approach is usually not employed because of its computational cost. In this paper we propose a method to combine the efficiency of single class localization with a subsequent decision process that works jointly for all given object classes. By following a multiple kernel learning (MKL) approach, we automatically obtain a sparse dependency graph of relevant object classes on which to base the decision. Experiments on the PASCAL VOC 2006 and 2007 datasets show that the subsequent joint decision step clearly improves the accuracy compared to single class detection.

ei

PDF ZIP Web DOI [BibTex]

PDF ZIP Web DOI [BibTex]


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Natural Evolution Strategies

Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J.

In CEC 2008, pages: 3381-3387, IEEE, Piscataway, NJ, USA, IEEE Congress on Evolutionary Computation, June 2008 (inproceedings)

Abstract
This paper presents natural evolution strategies (NES), a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural evolution strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with covariance matrix adaption (CMA), an evolution strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The natural evolution strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the dasiavanillapsila gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Partitioning of Image Datasets using Discriminative Context Information

Lampert, CH.

In CVPR 2008, pages: 1-8, IEEE Computer Society, Los Alamitos, CA, USA, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2008 (inproceedings)

Abstract
We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP‘s separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Correlational Spectral Clustering

Blaschko, MB., Lampert, CH.

In CVPR 2008, pages: 1-8, IEEE Computer Society, Los Alamitos, CA, USA, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2008 (inproceedings)

Abstract
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity measure, or by co-occurrence matrices that are then used in biclustering. In contrast, the proposed method uses separate similarity measures for each data representation, and allows for projection of previously unseen data that are only observed in one representation (e.g. images but not text). We show that this algorithm generalizes traditional spectral clustering algorithms and show consistent empirical improvement over spectral clustering on a variety of datasets of images with associated text.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Approximate Dynamic Programming with Gaussian Processes

Deisenroth, M., Peters, J., Rasmussen, C.

In ACC 2008, pages: 4480-4485, IEEE Service Center, Piscataway, NJ, USA, 2008 American Control Conference, June 2008 (inproceedings)

Abstract
In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems with continuous-valued state and control domains. Hence, approximations are often inevitable. The standard method of discretizing states and controls suffers from the curse of dimensionality and strongly depends on the chosen temporal sampling rate. In this paper, we introduce Gaussian process dynamic programming (GPDP) and determine an approximate globally optimal closed-loop policy. In GPDP, value functions in the Bellman recursion of the dynamic programming algorithm are modeled using Gaussian processes. GPDP returns an optimal statefeedback for a finite set of states. Based on these outcomes, we learn a possibly discontinuous closed-loop policy on the entire state space by switching between two independently trained Gaussian processes. A binary classifier selects one Gaussian process to predict the optimal control signal. We show that GPDP is able to yield an almost optimal solution to an LQ problem using few sample points. Moreover, we successfully apply GPDP to the underpowered pendulum swing up, a complex nonlinear control problem.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Beyond Sliding Windows: Object Localization by Efficient Subwindow Search

Lampert, C., Blaschko, M., Hofmann, T.

In CVPR 2008, pages: 1-8, IEEE Computer Society, Los Alamitos, CA, USA, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2008, Best paper award (inproceedings)

Abstract
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branchand- bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition.

ei

PDF PDF Web DOI [BibTex]

PDF PDF Web DOI [BibTex]


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Computed Torque Control with Nonparametric Regression Models

Nguyen-Tuong, D., Seeger, M., Peters, J.

In ACC 2008, pages: 212-217, IEEE Service Center, Piscataway, NJ, USA, 2008 American Control Conference, June 2008 (inproceedings)

Abstract
Computed torque control allows the design of considerably more precise, energy-efficient and compliant controls for robots. However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigid-body formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. In such cases, models approximated from robot data present an appealing alternative. In this paper, we compare two nonparametric regression methods for model approximation, i.e., locally weighted projection regression (LWPR) and Gaussian process regression (GPR). While locally weighted regression was employed for real-time model estimation in learning adaptive control, Gaussian process regression has not been used in control to-date due to high computational requirements. The comparison includes the assessment of model approximation for both regression methods using data originated from SARCOS robot arm, as well as an evaluation of the robot tracking p erformance in computed torque control employing the approximated models. Our results show that GPR can be applied for real-time control achieving higher accuracy. However, for the online learning LWPR is superior by reason of lower computational requirements.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Multi-Classification by Categorical Features via Clustering

Seldin, Y., Tishby, N.

In In the proceedings of the 25th International Conference on Machine Learning (ICML 2008), pages: 920-927, 25th International Conference on Machine Learning (ICML), June 2008 (inproceedings)

Abstract
We derive a generalization bound for multi-classification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space in the form of a Cartesian product of partitions for each of the parameters. The derived bound provides a means to evaluate clustering solutions in terms of the generalization power of a built-on classifier. For classification based on a single feature the bound serves to find a globally optimal classification rule. Comparison of the generalization power of individual features can then be used for feature ranking. Our experiments show that in this role the bound is much more precise than mutual information or normalized correlation indices.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Kernel Test of Nonlinear Granger Causality

Sun, X.

In Proceedings of the Workshop on Inference and Estimation in Probabilistic Time-Series Models, pages: 79-89, (Editors: Barber, D. , A. T. Cemgil, S. Chiappa), Isaac Newton Institute for Mathematical Sciences, Cambridge, United Kingdom, Workshop on Inference and Estimation in Probabilistic Time-Series Models, June 2008 (inproceedings)

Abstract
We present a novel test of nonlinear Granger causality in bivariate time series. The trace norm of conditional covariance operators is used to capture the prediction errors. Based on this measure, a subsampling-based multiple testing procedure tests the prediction improvement of one time series by the other one. The distributional properties of the resulting p-values reveal the direction of Granger causality. Encouraging results of experiments with simulated and real-world data support our approach.

ei

PDF [BibTex]

PDF [BibTex]


Bayesian Color Constancy Revisited
Bayesian Color Constancy Revisited

Gehler, P., Rother, C., Blake, A., Minka, T., Sharp, T.

In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, June 2008, http://dx.doi.org/10.1109/CVPR.2008.4587765 (inproceedings)

ei

website+code+data pdf [BibTex]

website+code+data pdf [BibTex]


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Real-time Learning of Resolved Velocity Control on a Mitsubishi PA-10

Peters, J., Nguyen-Tuong, D.

In ICRA 2008, pages: 2872-2877, IEEE Service Center, Piscataway, NJ, USA, 2008 IEEE International Conference on Robotics and Automation, May 2008 (inproceedings)

Abstract
Learning inverse kinematics has long been fascinating the robot learning community. While humans acquire this transformation to complicated tool spaces with ease, it is not a straightforward application for supervised learning algorithms due to non-convex learning problem. However, the key insight that the problem can be considered convex in small local regions allows the application of locally linear learning methods. Nevertheless, the local solution of the problem depends on the data distribution which can result into inconsistent global solutions with large model discontinuities. While this problem can be treated in various ways in offline learning, it poses a serious problem for online learning. Previous approaches to the real-time learning of inverse kinematics avoid this problem using smart data generation, such as the learner biasses its own solution. Such biassed solutions can result into premature convergence, and from the resulting solution it is often hard to understand what has been learned in tha t local region. This paper improves and solves this problem by presenting a learning algorithm which can deal with this inconsistency through re-weighting the data online. Furthermore, we show that our algorithms work not only in simulation, but we present real-time learning results on a physical Mitsubishi PA-10 robot arm.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Causal inference from statistical data

Sun, X.

Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex

Berens, P.

Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

ei

[BibTex]

[BibTex]


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Graph Mining with Variational Dirichlet Process Mixture Models

Tsuda, K., Kurihara, K.

In SDM 2008, pages: 432-442, (Editors: Zaki, M. J.), Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 8th SIAM International Conference on Data Mining, April 2008 (inproceedings)

Abstract
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraph patterns, the dimensionality gets too large for usual statistical methods. We propose a nonparametric Bayesian method for clustering graphs and selecting salient patterns at the same time. Variational inference is adopted here, because sampling is not applicable due to extremely high dimensionality. The feature set minimizing the free energy is efficiently collected with the DFS code tree, where the generation of useless subgraphs is suppressed by a tree pruning condition. In experiments, our method is compared with a simpler approach based on frequent subgraph mining, and graph kernels.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Model-Based Reinforcement Learning with Continuous States and Actions

Deisenroth, M., Rasmussen, C., Peters, J.

In ESANN 2008, pages: 19-24, (Editors: Verleysen, M. ), d-side, Evere, Belgium, European Symposium on Artificial Neural Networks, April 2008 (inproceedings)

Abstract
Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning Inverse Dynamics: A Comparison

Nguyen-Tuong, D., Peters, J., Seeger, M., Schölkopf, B.

In Advances in Computational Intelligence and Learning: Proceedings of the European Symposium on Artificial Neural Networks, pages: 13-18, (Editors: M Verleysen), d-side, Evere, Belgium, 16th European Symposium on Artificial Neural Networks (ESANN), April 2008 (inproceedings)

Abstract
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexities of manually obtaining sufficiently accurate models. In the past, learning has proven a viable alternative to using a combination of rigid-body dynamics and handcrafted approximations of nonlinearities. However, a major open question is what nonparametric learning method is suited best for learning dynamics? Traditionally, locally weighted projection regression (LWPR), has been the standard method as it is capable of online, real-time learning for very complex robots. However, while LWPR has had significant impact on learning in robotics, alternative nonparametric regression methods such as support vector regression (SVR) and Gaussian processes regression (GPR) offer interesting alternatives with fewer open parameters and potentially higher accuracy. In this paper, we evaluate these three alternatives for model learning. Our comparison consists out of the evaluation of learning quality for each regression method using original data from SARCOS robot arm, as well as the robot tracking performance employing learned models. The results show that GPR and SVR achieve a superior learning precision and can be applied for real-time control obtaining higher accuracy. However, for the online learning LWPR presents the better method due to its lower computational requirements.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Development and Application of a Python Scripting Framework for BCI2000

Schreiner, T.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

ei

[BibTex]

[BibTex]


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Efficient and Invariant Regularisation with Application to Computer Graphics

Walder, CJ.

Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

Abstract
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse problems which arise in, for example, machine learning and computer graphics, have been treated with practical success using methods based on a reproducing kernel Hilbert space perspective. This perspective is often theoretically convenient, in that many functional analysis problems reduce to linear algebra problems in these spaces. Somewhat more complex is the case of conditionally positive definite kernels, and we provide an introduction to both cases, deriving in a particularly elementary manner some key results for the conditionally positive definite case. A common complaint of the practitioner is the long running time of these kernel based algorithms. We provide novel ways of alleviating these problems by essentially using a non-standard function basis which yields computational advantages. That said, by doing so we must also forego the aforementioned theoretical conveniences, and hence need some additional analysis which we provide in order to make the approach practicable. We demonstrate that the method leads to state of the art performance on the problem of surface reconstruction from points. We also provide some analysis of kernels invariant to transformations such as translation and dilation, and show that this indicates the value of learning algorithms which use conditionally positive definite kernels. Correspondingly, we provide a few approaches for making such algorithms practicable. We do this either by modifying the kernel, or directly solving problems with conditionally positive definite kernels, which had previously only been solved with positive definite kernels. We demonstrate the advantage of this approach, in particular by attaining state of the art classification performance with only one free parameter.

ei

PDF [BibTex]

PDF [BibTex]


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Simulation and analysis of a passive pitch reversal flapping wing mechanism for an aerial robotic platform

Arabagi, V., Sitti, M.

In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages: 1260-1265, 2008 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Human movement generation based on convergent flow fields: A computational model and a behavioral experiment

Hoffmann, H., Schaal, S.

In Advances in Computational Motor Control VII, Symposium at the Society for Neuroscience Meeting, Washington DC, 2008, 2008, clmc (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


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Ab-initio Elektronentheorie der magnetischen Anisotropie im System FePt mit der Clusterentwicklungsmethode

Subkow, S.

Universität Stuttgart, Stuttgart, 2008 (mastersthesis)

mms

[BibTex]


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Pattern generators with sensory feedback for the control of quadruped locomotion

Righetti, L., Ijspeert, A.

In 2008 IEEE International Conference on Robotics and Automation, pages: 819-824, IEEE, Pasadena, USA, 2008 (inproceedings)

Abstract
Central pattern generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environment.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Experimental Study of Limit Cycle and Chaotic Controllers for the Locomotion of Centipede Robots

Matthey, L., Righetti, L., Ijspeert, A.

In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 1860-1865, IEEE, Nice, France, sep 2008 (inproceedings)

Abstract
In this contribution we present a CPG (central pattern generator) controller based on coupled Rossler systems. It is able to generate both limit cycle and chaotic behaviors through bifurcation. We develop an experimental test bench to measure quantitatively the performance of different controllers on unknown terrains of increasing difficulty. First, we show that for flat terrains, open loop limit cycle systems are the most efficient (in terms of speed of locomotion) but that they are quite sensitive to environmental changes. Second, we show that sensory feedback is a crucial addition for unknown terrains. Third, we show that the chaotic controller with sensory feedback outperforms the other controllers in very difficult terrains and actually promotes the emergence of short synchronized movement patterns. All that is done using an unified framework for the generation of limit cycle and chaotic behaviors, where a simple parameter change can switch from one behavior to the other through bifurcation. Such flexibility would allow the automatic adaptation of the robot locomotion strategy to the terrain uncertainty.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fabrication and Characterization of Biologically Inspired Mushroom-Shaped Elastomer Microfiber Arrays

Kim, S., Sitti, M.

In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pages: 839-847, 2008 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Gecko inspired micro-fibrillar adhesives for wall climbing robots on micro/nanoscale rough surfaces

Aksak, B., Murphy, M. P., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 3058-3063, 2008 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Miniature Mobile Robots Down to Micron Scale

Sitti, M.

In Micro-NanoMechatronics and Human Science, 2008. MHS 2008. International Symposium on, pages: 525-525, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields

Park, D., Hoffmann, H., Pastor, P., Schaal, S.

In IEEE International Conference on Humanoid Robots, 2008., 2008, clmc (inproceedings)

am

PDF [BibTex]

PDF [BibTex]


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Magnetism of amorphous and highly anisotropic multilayer systems on flat substrates and nanospheres

Amaladass, E. P.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

mms

link (url) [BibTex]


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Wetting and premelting of triple junctions and grain boundaries in the Al-Zn alloys

Straumal, B., Kogtenkova, O., Protasova, S., Mazilkin, A., Zieba, P., Czeppe, T., Wojewoda-Budka, J., Faryna, M.

In 495, pages: 126-131, Alicante, Spain, 2008 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Bose-Theorie der Dämpfung der Bewegung einer magnetischen Domänenwand

Hähnel, D.

Universität Stuttgart, Stuttgart, 2008 (mastersthesis)

mms

[BibTex]

[BibTex]


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The dual role of uncertainty in force field learning

Mistry, M., Theodorou, E., Hoffmann, H., Schaal, S.

In Abstracts of the Eighteenth Annual Meeting of Neural Control of Movement (NCM), Naples, Florida, April 29-May 4, 2008, clmc (inproceedings)

Abstract
Force field experiments have been a successful paradigm for studying the principles of planning, execution, and learning in human arm movements. Subjects have been shown to cope with the disturbances generated by force fields by learning internal models of the underlying dynamics to predict disturbance effects or by increasing arm impedance (via co-contraction) if a predictive approach becomes infeasible. Several studies have addressed the issue uncertainty in force field learning. Scheidt et al. demonstrated that subjects exposed to a viscous force field of fixed structure but varying strength (randomly changing from trial to trial), learn to adapt to the mean disturbance, regardless of the statistical distribution. Takahashi et al. additionally show a decrease in strength of after-effects after learning in the randomly varying environment. Thus they suggest that the nervous system adopts a dual strategy: learning an internal model of the mean of the random environment, while simultaneously increasing arm impedance to minimize the consequence of errors. In this study, we examine what role variance plays in the learning of uncertain force fields. We use a 7 degree-of-freedom exoskeleton robot as a manipulandum (Sarcos Master Arm, Sarcos, Inc.), and apply a 3D viscous force field of fixed structure and strength randomly selected from trial to trial. Additionally, in separate blocks of trials, we alter the variance of the randomly selected strength multiplier (while keeping a constant mean). In each block, after sufficient learning has occurred, we apply catch trials with no force field and measure the strength of after-effects. As expected in higher variance cases, results show increasingly smaller levels of after-effects as the variance is increased, thus implying subjects choose the robust strategy of increasing arm impedance to cope with higher levels of uncertainty. Interestingly, however, subjects show an increase in after-effect strength with a small amount of variance as compared to the deterministic (zero variance) case. This result implies that a small amount of variability aides in internal model formation, presumably a consequence of the additional amount of exploration conducted in the workspace of the task.

am

[BibTex]

[BibTex]


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Dynamic movement primitives for movement generation motivated by convergent force fields in frog

Hoffmann, H., Pastor, P., Schaal, S.

In Adaptive Motion of Animals and Machines (AMAM), 2008, clmc (inproceedings)

am

PDF [BibTex]

PDF [BibTex]


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Polymeric Micro/Nanofiber Manufacturing and Mechanical Characterization

Nain, A. S., Sitti, M., Amon, C.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 295-303, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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An untethered magnetically actuated micro-robot capable of motion on arbitrary surfaces

Floyd, S., Pawashe, C., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 419-424, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Fabrication of bio-inspired elastomer nanofiber arrays with spatulate tips using notching effect

Kim, S., Sitti, M., Jang, J., Thomas, E. L.

In Nanotechnology, 2008. NANO’08. 8th IEEE Conference on, pages: 780-782, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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A motorized anchoring mechanism for a tethered capsule robot using fibrillar adhesives for interventions in the esophagus

Glass, P., Cheung, E., Wang, H., Appasamy, R., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, pages: 758-764, 2008 (inproceedings)

pi

[BibTex]

[BibTex]


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

al

DOI [BibTex]

DOI [BibTex]