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2005


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Splines with non positive kernels

Canu, S., Ong, CS., Mary, X.

In 5th International ISAAC Congress, pages: 1-10, (Editors: Begehr, H. G.W., F. Nicolosi), World Scientific, Singapore, 5th International ISAAC Congress, July 2005 (inproceedings)

Abstract
Non parametric regressions methods can be presented in two main clusters. The one of smoothing splines methods requiring positive kernels and the other one known as Nonparametric Kernel Regression allowing the use of non positive kernels such as the Epanechnikov kernel. We propose a generalization of the smoothing spline method to include kernels which are still symmetric but not positive semi definite (they are called indefinite). The general relationship between smoothing spline, Reproducing Kernel Hilbert Spaces and positive kernels no longer exists with indefinite kernel. Instead they are associated with functional spaces called Reproducing Kernel Krein Spaces (RKKS) embedded with an indefinite inner product and thus not directly associated with a norm. Smothing splines in RKKS have many of the interesting properties of splines in RKHS, such as orthogon ality, projection, representer theorem and generalization bounds. We show that smoothing splines can be defined in RKKS as the regularized solution of the interpolation problem. Since no norm is available in a RKKS, Tikhonov regularization cannot be defined. Instead, we proposed to use iterative methods of conjugate gradient type with early stopping as regularization mechanism. Several iterative algorithms were collected which can be used to solve the optimization problems associated with learning in indefinite spaces. Some preliminary experiments with indefinite kernels for spline smoothing are reported revealing the computational efficiency of the approach.

ei

PDF Web [BibTex]

2005


PDF Web [BibTex]


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Kernel Methods for Implicit Surface Modeling

Schölkopf, B., Giesen, J., Spalinger, S.

In Advances in Neural Information Processing Systems 17, pages: 1193-1200, (Editors: LK Saul and Y Weiss and L Bottou), MIT Press, Cambridge, MA, USA, 18th Annual Conference on Neural Information Processing Systems (NIPS), July 2005 (inproceedings)

Abstract
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space and then determining regions in terms of hyperplanes.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Comparative evaluation of Independent Components Analysis algorithms for isolating target-relevant information in brain-signal classification

Hill, N., Schröder, M., Lal, T., Schölkopf, B.

Brain-Computer Interface Technology, 3, pages: 95, June 2005 (poster)

ei

PDF [BibTex]


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Classification of natural scenes using global image statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

47, pages: 88, 47. Tagung Experimentell Arbeitender Psychologen, April 2005 (poster)

ei

[BibTex]

[BibTex]


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Classification of Natural Scenes using Global Image Statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

8, pages: 88, 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and nonanimal images. After applying a DFT to the image, we put the Fourier spectrum of each image into 48 bins (8 orientations with 6 frequency bands). Using all of these bins, classification performance on the Fourier spectrum reached 70%. In an iterative procedure, we then removed the bins whose absence caused the smallest damage to the classification performance (one bin per iteration). Notably, performance stayed at about 70% until less then 6 bins were left. A detailed analysis of the classification weights showed that a comparatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical orientations at the highest spatial frequency band. When using only a single frequency band (8 bins) we found that 67% classification performance could be reached when only the high spatial frequency information was used, which decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially pre-filtered images. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

ei

Web [BibTex]

Web [BibTex]


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Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, T., Hill, N., Rasmussen, C., Wichmann, F.

8, pages: 109, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
A psychometric function can be described by its shape and four parameters: position or threshold, slope or width, false alarm rate or chance level, and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. We introduce a new adaptive bayesian psychometric method which collects data for any set of parameters with high efficency. It places trials by minimizing the expected entropy [1] of the posterior pdf over a set of possible stimuli. In contrast to most other adaptive methods it is neither limited to threshold measurement nor to forced-choice designs. Nuisance parameters can be included in the estimation and lead to less biased estimates. The method supports block designs which do not harm the performance when a sufficient number of trials are performed. Block designs are useful for control of response bias and short term performance shifts such as adaptation. We present the results of evaluations of the method by computer simulations and experiments with human observers. In the simulations we investigated the role of parametric assumptions, the quality of different point estimates, the effect of dynamic termination criteria and many other settings. [1] Kontsevich, L.L. and Tyler, C.W. (1999): Bayesian adaptive estimation of psychometric slope and threshold. Vis. Res. 39 (16), 2729-2737.

ei

Web [BibTex]

Web [BibTex]


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Bayesian Inference for Psychometric Functions

Kuss, M., Jäkel, F., Wichmann, F.

8, pages: 106, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
In psychophysical studies of perception the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. We propose the use of Bayesian inference to extract the information contained in experimental data to learn about the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we use a Markov chain Monte Carlo method to generate samples from the posterior distribution over parameters. These samples can be used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. We compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with parametric bootstrap techniques for confidence interval estimation. Experiments indicate that Bayesian inference methods are superior to bootstrap-based methods and are thus the method of choice for estimating the psychometric function and its confidence-intervals.

ei

Web [BibTex]

Web [BibTex]


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Active Learning for Parzen Window Classifier

Chapelle, O.

In AISTATS 2005, pages: 49-56, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
The problem of active learning is approached in this paper by minimizing directly an estimate of the expected test error. The main difficulty in this ``optimal'' strategy is that output probabilities need to be estimated accurately. We suggest here different methods for estimating those efficiently. In this context, the Parzen window classifier is considered because it is both simple and probabilistic. The analysis of experimental results highlights that regularization is a key ingredient for this strategy.

ei

Web [BibTex]

Web [BibTex]


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Semi-Supervised Classification by Low Density Separation

Chapelle, O., Zien, A.

In AISTATS 2005, pages: 57-64, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary in low density regions, by gradient descent; 3. combining the first two to make maximum use of the cluster assumption. We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Pauls, J., Schölkopf, B., Logothetis, N.

In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pages: 112-119, (Editors: R Cowell, R and Z Ghahramani), AISTATS, January 2005 (inproceedings)

Abstract
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

In AISTATS 2005, pages: 136-143, (Editors: Cowell, R. , Z. Ghahramani), Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics), January 2005 (inproceedings)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and two image classification problems.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Intrinsic Dimensionality Estimation of Submanifolds in Euclidean space

Hein, M., Audibert, Y.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 289 , (Editors: De Raedt, L. , S. Wrobel), ICML Bonn, 2005 (inproceedings)

Abstract
We present a new method to estimate the intrinsic dimensionality of a submanifold M in Euclidean space from random samples. The method is based on the convergence rates of a certain U-statistic on the manifold. We solve at least partially the question of the choice of the scale of the data. Moreover the proposed method is easy to implement, can handle large data sets and performs very well even for small sample sizes. We compare the proposed method to two standard estimators on several artificial as well as real data sets.

ei

PDF [BibTex]

PDF [BibTex]


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Large Scale Genomic Sequence SVM Classifiers

Sonnenburg, S., Rätsch, G., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 849-856, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve sufficiently high classification performances. In this work we study two recently proposed and successfully used kernels, namely the Spectrum kernel and the Weighted Degree kernel (WD). In particular, we suggest several extensions using Suffix Trees and modi cations of an SMO-like SVM training algorithm in order to accelerate the training of the SVMs and their evaluation on test sequences. Our simulations show that for the spectrum kernel and WD kernel, large scale SVM training can be accelerated by factors of 20 and 4 times, respectively, while using much less memory (e.g. no kernel caching). The evaluation on new sequences is often several thousand times faster using the new techniques (depending on the number of Support Vectors). Our method allows us to train on sets as large as one million sequences.

ei

PDF [BibTex]

PDF [BibTex]


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O.

In Proceedings of the 8th InternationalWork-Conference on Artificial Neural Networks, LNCS 3512, pages: 176-191, (Editors: J Cabestany and A Prieto and F Sandoval), Springer, Berlin Heidelberg, Germany, IWANN, 2005 (inproceedings)

Abstract
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension, e.g., thousands of dimensions. This is achieved by mapping the objects into continuous or discrete spaces, using joint kernels. Known correlations between input and output can be defined by such kernels, some of which can maintain linearity in the outputs to provide simple (closed form) pre-images. We provide examples of such kernels and empirical results.

ei

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Analysis of Some Methods for Reduced Rank Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

In Switching and Learning in Feedback Systems, pages: 98-127, (Editors: Murray Smith, R. , R. Shorten), Springer, Berlin, Germany, European Summer School on Multi-Agent Control, 2005 (inproceedings)

Abstract
While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning the covariance function hyperparameters and the support set. We propose a method for learning hyperparameters for a given support set. We also review the Sparse Greedy GP (SGGP) approximation (Smola and Bartlett, 2001), which is a way of learning the support set for given hyperparameters based on approximating the posterior. We propose an alternative method to the SGGP that has better generalization capabilities. Finally we make experiments to compare the different ways of training a RRGP. We provide some Matlab code for learning RRGPs.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Global image statistics of natural scenes

Drewes, J., Wichmann, F., Gegenfurtner, K.

Bioinspired Information Processing, 08, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]


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From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

Hein, M., Audibert, J., von Luxburg, U.

In Proceedings of the 18th Conference on Learning Theory (COLT), pages: 470-485, Conference on Learning Theory, 2005, Student Paper Award (inproceedings)

Abstract
In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the sample size increases. Even though this assertion serves as a justification for many Laplacian-based algorithms, so far only some aspects of this claim have been rigorously proved. In this paper we close this gap by establishing the strong pointwise consistency of a family of graph Laplacians with data-dependent weights to some weighted Laplace operator. Our investigation also includes the important case where the data lies on a submanifold of $R^d$.

ei

PDF [BibTex]

PDF [BibTex]


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Propagating Distributions on a Hypergraph by Dual Information Regularization

Tsuda, K.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 921 , (Editors: De Raedt, L. , S. Wrobel), ICML Bonn, 2005 (inproceedings)

Abstract
In the information regularization framework by Corduneanu and Jaakkola (2005), the distributions of labels are propagated on a hypergraph for semi-supervised learning. The learning is efficiently done by a Blahut-Arimoto-like two step algorithm, but, unfortunately, one of the steps cannot be solved in a closed form. In this paper, we propose a dual version of information regularization, which is considered as more natural in terms of information geometry. Our learning algorithm has two steps, each of which can be solved in a closed form. Also it can be naturally applied to exponential family distributions such as Gaussians. In experiments, our algorithm is applied to protein classification based on a metabolic network and known functional categories.

ei

[BibTex]

[BibTex]


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A Brain Computer Interface with Online Feedback based on Magnetoencephalography

Lal, T., Schröder, M., Hill, J., Preissl, H., Hinterberger, T., Mellinger, J., Bogdan, M., Rosenstiel, W., Hofmann, T., Birbaumer, N., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 465-472, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto- noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a “proof of concept”.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Healing the Relevance Vector Machine through Augmentation

Rasmussen, CE., Candela, JQ.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 689 , (Editors: De Raedt, L. , S. Wrobel), ICML, 2005 (inproceedings)

Abstract
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties have the unintuitive property, that emph{they get smaller the further you move away from the training cases}. We give a thorough analysis. Inspired by the analogy to non-degenerate Gaussian Processes, we suggest augmentation to solve the problem. The purpose of the resulting model, RVM*, is primarily to corroborate the theoretical and experimental analysis. Although RVM* could be used in practical applications, it is no longer a truly sparse model. Experiments show that sparsity comes at the expense of worse predictive distributions.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Kernel-Methods, Similarity, and Exemplar Theories of Categorization

Jäkel, F., Wichmann, F.

ASIC, 4, 2005 (poster)

Abstract
Kernel-methods are popular tools in machine learning and statistics that can be implemented in a simple feed-forward neural network. They have strong connections to several psychological theories. For example, Shepard‘s universal law of generalization can be given a kernel interpretation. This leads to an inner product and a metric on the psychological space that is different from the usual Minkowski norm. The metric has psychologically interesting properties: It is bounded from above and does not have additive segments. As categorization models often rely on Shepard‘s law as a model for psychological similarity some of them can be recast as kernel-methods. In particular, ALCOVE is shown to be closely related to kernel logistic regression. The relationship to the Generalized Context Model is also discussed. It is argued that functional analysis which is routinely used in machine learning provides valuable insights also for psychology.

ei

Web [BibTex]


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Rapid animal detection in natural scenes: critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Experimentelle Psychologie. Beitr{\"a}ge zur 47. Tagung experimentell arbeitender Psychologen, 47, pages: 225, 2005 (poster)

ei

[BibTex]

[BibTex]


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Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach

Jung, T., Herrera, L., Schölkopf, B.

In Proceedings of the 8th International Work-Conferenceon Artificial Neural Networks (Computational Intelligence and Bioinspired Systems), Lecture Notes in Computer Science, Vol. 3512, LNCS 3512, pages: 960-967, (Editors: J Cabestany and A Prieto and F Sandoval), Springer, Berlin Heidelberg, Germany, IWANN, 2005 (inproceedings)

Abstract
In this paper we report the results obtained using a kernel-based approach to predict the temporal development of four response signals in the process control of a glass melting tank with 16 input parameters. The data set is a revised version1 from the modelling challenge in EUNITE-2003. The central difficulties are: large time-delays between changes in the inputs and the outputs, large number of data, and a general lack of knowledge about the relevant variables that intervene in the process. The methodology proposed here comprises Support Vector Machines (SVM) and Regularization Networks (RN). We use the idea of sparse approximation both as a means of regularization and as a means of reducing the computational complexity. Furthermore, we will use an incremental approach to add new training examples to the kernel-based method and efficiently update the current solution. This allows us to use a sophisticated learning scheme, where we iterate between prediction and training, with good computational efficiency and satisfactory results.

ei

DOI [BibTex]

DOI [BibTex]


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Object correspondence as a machine learning problem

Schölkopf, B., Steinke, F., Blanz, V.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 777-784, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points of one object will be mapped to ``similar‘‘ points on the other one. Our system, which contains little engineering or domain knowledge, delivers state of the art performance. We present application results including close to photorealistic morphs of 3D head models.

ei

PDF [BibTex]

PDF [BibTex]


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The human brain as large margin classifier

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE), 2, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]


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Implicit Surface Modelling as an Eigenvalue Problem

Walder, C., Chapelle, O., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 937-944, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)

Abstract
We discuss the problem of fitting an implicit shape model to a set of points sampled from a co-dimension one manifold of arbitrary topology. The method solves a non-convex optimisation problem in the embedding function that defines the implicit by way of its zero level set. By assuming that the solution is a mixture of radial basis functions of varying widths we attain the globally optimal solution by way of an equivalent eigenvalue problem, without using or constructing as an intermediate step the normal vectors of the manifold at each data point. We demonstrate the system on two and three dimensional data, with examples of missing data interpolation and set operations on the resultant shapes.

ei

PDF [BibTex]

PDF [BibTex]


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Natural Actor-Critic

Peters, J., Vijayakumar, S., Schaal, S.

In Proceedings of the 16th European Conference on Machine Learning, 3720, pages: 280-291, (Editors: Gama, J.;Camacho, R.;Brazdil, P.;Jorge, A.;Torgo, L.), Springer, ECML, 2005, clmc (inproceedings)

Abstract
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing AmariÕs natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regres- sion. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and BradtkeÕs Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Em- pirical evaluations illustrate the effectiveness of our techniques in com- parison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Comparative experiments on task space control with redundancy resolution

Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S.

In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3901-3908, Edmonton, Alberta, Canada, Aug. 2-6, IROS, 2005, clmc (inproceedings)

Abstract
Understanding the principles of motor coordination with redundant degrees of freedom still remains a challenging problem, particularly for new research in highly redundant robots like humanoids. Even after more than a decade of research, task space control with redundacy resolution still remains an incompletely understood theoretical topic, and also lacks a larger body of thorough experimental investigation on complex robotic systems. This paper presents our first steps towards the development of a working redundancy resolution algorithm which is robust against modeling errors and unforeseen disturbances arising from contact forces. To gain a better understanding of the pros and cons of different approaches to redundancy resolution, we focus on a comparative empirical evaluation. First, we review several redundancy resolution schemes at the velocity, acceleration and torque levels presented in the literature in a common notational framework and also introduce some new variants of these previous approaches. Second, we present experimental comparisons of these approaches on a seven-degree-of-freedom anthropomorphic robot arm. Surprisingly, one of our simplest algorithms empirically demonstrates the best performance, despite, from a theoretical point, the algorithm does not share the same beauty as some of the other methods. Finally, we discuss practical properties of these control algorithms, particularly in light of inevitable modeling errors of the robot dynamics.

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A piezoelectric unimorph actuator based precision positioning miniature walking robot

Son, K. J., Kartik, V., Wickert, J. A., Sitti, M.

In Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pages: 176-182, 2005 (inproceedings)

[BibTex]

[BibTex]


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Modeling and testing of a biomimetic flagellar propulsion method for microscale biomedical swimming robots

Behkam, B., Sitti, M.

In Proceedings of Advanced Intelligent Mechatronics Conference, pages: 37-42, 2005 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares

Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S.

In Advances in Neural Information Processing Systems 18 (NIPS 2005), (Editors: Weiss, Y.;Schölkopf, B.;Platt, J.), Cambridge, MA: MIT Press, Vancouver, BC, Dec. 6-11, 2005, clmc (inproceedings)

Abstract
An increasing number of projects in neuroscience requires the statistical analysis of high dimensional data sets, as, for instance, in predicting behavior from neural firing, or in operating artificial devices from brain recordings in brain-machine interfaces. Linear analysis techniques remain prevalent in such cases, but classi-cal linear regression approaches are often numercially too fragile in high dimen-sions. In this paper, we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed with linear ap-proaches from neural activity in primary motor cortex (M1). To achieve robust data analysis, we develop a full Bayesian approach to linear regression that automatically detects and excludes irrelevant features in the data, and regular-izes against overfitting. In comparison with ordinary least squares, stepwise re-gression, partial least squares, and a brute force combinatorial search for the most predictive input features in the data, we demonstrate that the new Bayesian method offers a superior mixture of characteristics in terms of regularization against overfitting, computational efficiency, and ease of use, demonstrating its potential as a drop-in replacement for other linear regression techniques. As neuroscientific results, our analyses demonstrate that EMG data can be well pre-dicted from M1 neurons, further opening the path for possible real-time inter-faces between brains and machines.

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link (url) [BibTex]

link (url) [BibTex]


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Biologically inspired adhesion based surface climbing robots

Menon, C., Sitti, M.

In Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pages: 2715-2720, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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Claytronics: highly scalable communications, sensing, and actuation networks

Aksak, Burak, Bhat, Preethi Srinivas, Campbell, Jason, DeRosa, Michael, Funiak, Stanislav, Gibbons, Phillip B, Goldstein, Seth Copen, Guestrin, Carlos, Gupta, Ashish, Helfrich, Casey, others

In Proceedings of the 3rd international conference on Embedded networked sensor systems, pages: 299-299, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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Rapbid synchronization and accurate phase-locking of rhythmic motor primitives

Pongas, D., Billard, A., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 2911-2916, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

Abstract
Rhythmic movement is ubiquitous in human and animal behavior, e.g., as in locomotion, dancing, swimming, chewing, scratching, music playing, etc. A particular feature of rhythmic movement in biology is the rapid synchronization and phase locking with other rhythmic events in the environment, for instance music or visual stimuli as in ball juggling. In traditional oscillator theories to rhythmic movement generation, synchronization with another signal is relatively slow, and it is not easy to achieve accurate phase locking with a particular feature of the driving stimulus. Using a recently developed framework of dynamic motor primitives, we demonstrate a novel algorithm for very rapid synchronizaton of a rhythmic movement pattern, which can phase lock any feature of the movement to any particulur event in the driving stimulus. As an example application, we demonstrate how an anthropomorphic robot can use imitation learning to acquire a complex rumming pattern and keep it synchronized with an external rhythm generator that changes its frequency over time.

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link (url) [BibTex]

link (url) [BibTex]


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Magnetization reversal behavior of nanogranular CoCrPt alloy thin films studied with magnetic transmission X-ray microscopy

Fischer, P., Im, M., Eimüller, T., Schütz, G., Shin, S.

In 286, pages: 311-314, Boulder, CO, USA, 2005 (inproceedings)

mms

[BibTex]

[BibTex]


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Biologically Inspired Miniature Water Strider Robot.

Suhr, S. H., Song, Y. S., Lee, S. J., Sitti, M.

In Robotics: Science and Systems, pages: 319-326, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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Polymer micro/nanofiber fabrication using micro/nanopipettes

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

In Nanotechnology, 2005. 5th IEEE Conference on, pages: 366-369, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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A dynamical systems approach to learning: a frequency-adaptive hopper robot

Buchli, J., Righetti, L., Ijspeert, A.

In Proceedings of the VIIIth European Conference on Artificial Life ECAL 2005, pages: 210-220, Springer Verlag, 2005 (inproceedings)

mg

[BibTex]

[BibTex]


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From Dynamic Hebbian Learning for Oscillators to Adaptive Central Pattern Generators

Righetti, L., Buchli, J., Ijspeert, A.

In Proceedings of 3rd International Symposium on Adaptive Motion in Animals and Machines – AMAM 2005, Verlag ISLE, Ilmenau, 2005 (inproceedings)

mg

[BibTex]

[BibTex]


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A new methodology for robot control design

Peters, J., Mistry, M., Udwadia, F. E., Schaal, S.

In The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005), Long Beach, CA, Sept. 24-28, 2005, clmc (inproceedings)

Abstract
Gauss principle of least constraint and its generalizations have provided a useful insights for the development of tracking controllers for mechanical systems (Udwadia,2003). Using this concept, we present a novel methodology for the design of a specific class of robot controllers. With our new framework, we demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic framework, and show experimental verifications on a Sarcos Master Arm robot for some of these controllers. We believe that the suggested approach unifies and simplifies the design of optimal nonlinear control laws for robots obeying rigid body dynamics equations, both with or without external constraints, holonomic or nonholonomic constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

am

link (url) [BibTex]

link (url) [BibTex]


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Fusion of biomedical microcapsule endoscope and microsystem technology

Kim, Tae Song, Kim, Byungkyu, Cho, Dongil Dan, Song, Si Young, Dario, P, Sitti, M

In Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS’05. The 13th International Conference on, 1, pages: 9-14, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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Atomic force microscope based two-dimensional assembly of micro/nanoparticles

Tafazzoli, A., Pawashe, C., Sitti, M.

In Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005.(ISATP 2005). The 6th IEEE International Symposium on, pages: 230-235, 2005 (inproceedings)

pi

[BibTex]

[BibTex]


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Defects distribution of Pr2Fe14B hard magnetic magnet from amorphous to nanostructures characterized by positron annihilation spectroscopy

Wu, Y. C., Sprengel, W., Reimann, K., Reichle, K. J., Goll, D., Würschum, R., Schaefer, H. E.

In PRICM 5. Proceedings of the Fifth Pacific RIM International Conference on Advanced Materials and Processing, 475-479, pages: 2123-2126, Materials Science Forum, Trans Tech, Beijing, China, 2005 (inproceedings)

mms

[BibTex]

[BibTex]


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Implementing sub-ns time resolution into magnetic X-ray microscopies

Puzic, A., Stoll, H., Fischer, P., Van Waeyenberge, B., Raabe, J., Denbeaux, G., Haug, T., Weiss, D., Schütz, G.

In T115, pages: 1029-1031, Malmö/Lund, Sweden, 2005 (inproceedings)

mms

[BibTex]

[BibTex]


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Arm movement experiments with joint space force fields using an exoskeleton robot

Mistry, M., Mohajerian, P., Schaal, S.

In IEEE Ninth International Conference on Rehabilitation Robotics, pages: 408-413, Chicago, Illinois, June 28-July 1, 2005, clmc (inproceedings)

Abstract
A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3-D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and can minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects' arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while post-learning hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that besides learning a model of the force field, the nervous system also chose to exploit the space to minimize the effects of the force field on the realization of the endpoint trajectory plan. Further applications for our apparatus include studies in motor system redundancy resolution and inverse kinematics, as well as rehabilitation.

am

link (url) [BibTex]

link (url) [BibTex]


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A unifying framework for the control of robotics systems

Peters, J., Mistry, M., Udwadia, F. E., Cory, R., Nakanishi, J., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 1824-1831, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

Abstract
Recently, [1] suggested to derive tracking controllers for mechanical systems using a generalization of GaussÕ principle of least constraint. This method al-lows us to reformulate control problems as a special class of optimal control. We take this line of reasoning one step further and demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sar-cos Master Arm robot for some of the the derived controllers.We believe that the suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equa-tions, both with or without external constraints, with over-actuation or under-actuation, as well as open-chain and closed-chain kinematics.

am

link (url) [BibTex]

link (url) [BibTex]


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A new endoscopic microcapsule robot using beetle inspired microfibrillar adhesives

Cheung, E., Karagozler, M. E., Park, S., Kim, B., Sitti, M.

In Advanced Intelligent Mechatronics. Proceedings, 2005 IEEE/ASME International Conference on, pages: 551-557, 2005 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Learning to Feel the Physics of a Body

Der, R., Hesse, F., Martius, G.

In Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 , 2, pages: 252-257, Washington, DC, USA, 2005 (inproceedings)

Abstract
Despite the tremendous progress in robotic hardware and in both sensorial and computing efficiencies the performance of contemporary autonomous robots is still far below that of simple animals. This has triggered an intensive search for alternative approaches to the control of robots. The present paper exemplifies a general approach to the self-organization of behavior which has been developed and tested in various examples in recent years. We apply this approach to an underactuated snake like artifact with a complex physical behavior which is not known to the controller. Due to the weak forces available, the controller so to say has to develop a kind of feeling for the body which is seen to emerge from our approach in a natural way with meandering and rotational collective modes being observed in computer simulation experiments.

al

[BibTex]

[BibTex]