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2010


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The Influence of the Image Basis on Modeling and Steganalysis Performance

Schwamberger, V., Le, P., Schölkopf, B., Franz, M.

In Information Hiding, pages: 133-144, (Editors: R Böhme and PWL Fong and R Safavi-Naini), Springer, Berlin, Germany, 12th international Workshop (IH), June 2010 (inproceedings)

Abstract
We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the image model is used to predict coefficient values from their neighborhoods, and the final classification step uses statistical descriptors of the residual. Our findings are counter-intuitive on first sight: Although Laplacian pyramids have better image modeling capabilities than wavelets, steganalysis based on wavelets is much more successful. We present a number of experiments that suggest possible explanations for this result.

ei

PDF Web DOI [BibTex]

2010


PDF Web DOI [BibTex]


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A PAC-Bayesian Analysis of Co-clustering, Graph Clustering, and Pairwise Clustering

Seldin, Y.

In ICML 2010 Workshop on Social Analytics: Learning from human interactions, pages: 1-5, ICML Workshop on Social Analytics: Learning from human interactions, June 2010 (inproceedings)

Abstract
We review briefly the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008, 2009, 2010), which provided generalization guarantees and regularization terms absent in the preceding formulations of this problem and achieved state-of-the-art prediction results in MovieLens collaborative filtering task. Inspired by this analysis we formulate weighted graph clustering1 as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. Following the lines of (Seldin and Tishby, 2010) we derive PAC-Bayesian generalization bounds for graph clustering. The bounds show that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering a more accurate way to deal with finite sample issues.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Solving Large-Scale Nonnegative Least Squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Han- son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by ex- ploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established con- vex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Matrix Approximation Problems

Sra, S.

EU Regional School: Rheinisch-Westf{\"a}lische Technische Hochschule Aachen, May 2010 (talk)

ei

PDF AVI [BibTex]

PDF AVI [BibTex]


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BCI2000 and Python

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract
A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 realtime software package.

ei

PDF [BibTex]

PDF [BibTex]


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Extending BCI2000 Functionality with Your Own C++ Code

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract
A tutorial, with exercises, on how to use BCI2000 C++ framework to write your own real-time signal-processing modules.

ei

[BibTex]

[BibTex]


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Reinforcement learning of motor skills in high dimensions: A path integral approach

Theodorou, E., Buchli, J., Schaal, S.

In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages: 2397-2403, May 2010, clmc (inproceedings)

Abstract
Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far due to the computational difficulties that reinforcement learning encounters in high dimensional continuous state-action spaces. In this paper, we derive a novel approach to RL for parameterized control policies based on the framework of stochastic optimal control with path integrals. While solidly grounded in optimal control theory and estimation theory, the update equations for learning are surprisingly simple and have no danger of numerical instabilities as neither matrix inversions nor gradient learning rates are required. Empirical evaluations demonstrate significant performance improvements over gradient-based policy learning and scalability to high-dimensional control problems. Finally, a learning experiment on a robot dog illustrates the functionality of our algorithm in a real-world scenario. We believe that our new algorithm, Policy Improvement with Path Integrals (PI2), offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL in robotics.

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link (url) [BibTex]

link (url) [BibTex]


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Inverse dynamics control of floating base systems using orthogonal decomposition

Mistry, M., Buchli, J., Schaal, S.

In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages: 3406-3412, May 2010, clmc (inproceedings)

Abstract
Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e.g., humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.

am

link (url) [BibTex]

link (url) [BibTex]


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Fast, robust quadruped locomotion over challenging terrain

Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., Schaal, S.

In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages: 2665-2670, May 2010, clmc (inproceedings)

Abstract
We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrain of varying difficulty levels. We demonstrate the generalization ability of this controller by presenting test results from an independent external test team on terrains that have never been shown to us.

am

link (url) [BibTex]

link (url) [BibTex]


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Apprenticeship learning via soft local homomorphisms

Boularias, A., Chaib-Draa, B.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 2971-2976, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

Abstract
We consider the problem of apprenticeship learning when the expert's demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient solution to this problem based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). However, past work on IRL requires an accurate estimate of the frequency of encountering each feature of the states when the robot follows the expert‘s policy. Given that the complete policy of the expert is unknown, the features frequencies can only be empirically estimated from the demonstrated trajectories. In this paper, we propose to use a transfer method, known as soft homomorphism, in order to generalize the expert‘s policy to unvisited regions of the state space. The generalized policy can be used either as the robot‘s final policy, or to calculate the features frequencies within an IRL algorithm. Empirical results show that our approach is able to learn good policies from a small number of demonstrations.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Using Model Knowledge for Learning Inverse Dynamics

Nguyen-Tuong, D., Peters, J.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 2677-2682, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

Abstract
In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the system. For many complex robot systems, available prior knowledge from advanced physics-based modeling techniques can entail valuable information for model learning that may result in faster learning speed, higher accuracy and better generalization. In this paper, we investigate how parametric physical models (e.g., obtained from rigid body dynamics) can be used to improve the learning performance, and, especially, how semiparametric regression methods can be applied in this context. We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process regression. We compare the learning performance o f these methods first on sampled data and, subsequently, apply the obtained inverse dynamics models in tracking control on a real Barrett WAM. The results show that the semiparametric models learned with rigid body dynamics as prior outperform the standard rigid body dynamics models on real data while generalizing better for unknown parts of the state space.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Coherent Inference on Optimal Play in Game Trees

Hennig, P., Stern, D., Graepel, T.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 326-333, (Editors: Teh, Y.W. , M. Titterington ), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Round-based games are an instance of discrete planning problems. Some of the best contemporary game tree search algorithms use random roll-outs as data. Relying on a good policy, they learn on-policy values by propagating information upwards in the tree, but not between sibling nodes. Here, we present a generative model and a corresponding approximate message passing scheme for inference on the optimal, off-policy value of nodes in smooth AND/OR trees, given random roll-outs. The crucial insight is that the distribution of values in game trees is not completely arbitrary. We define a generative model of the on-policy values using a latent score for each state, representing the value under the random roll-out policy. Inference on the values under the optimal policy separates into an inductive, pre-data step and a deductive, post-data part. Both can be solved approximately with Expectation Propagation, allowing off-policy value inference for any node in the (exponentially big) tree in linear time.

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PDF Web [BibTex]

PDF Web [BibTex]


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Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D., Peters, J.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 557-564, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component which cannot be achieved by straightforward usage of off-the-shelf machine learning methods (such as Gaussian process regression or support vector regression). In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The proposed approach combines a sparsification method based on an independence measure with a large scale database. In combination with an incremental learning approach such as sequential support vector regression, we obtain a regression method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real robot emphasizes the applicability of the proposed approach in real-time online model learning for real world systems.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Multitask Learning for Brain-Computer Interfaces

Alamgir, M., Grosse-Wentrup, M., Altun, Y.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 17-24, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics , May 2010 (inproceedings)

Abstract
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subjectspecific calibration data prior to actual use of the BCI for communication. In this paper, we utilize the framework of multitask learning to construct a BCI that can be used without any subject-specific calibration process. We discuss how this out-of-the-box BCI can be further improved in a computationally efficient manner as subject-specific data becomes available. The feasibility of the approach is demonstrated on two sets of experimental EEG data recorded during a standard two-class motor imagery paradigm from a total of 19 healthy subjects. Specifically, we show that satisfactory classification results can be achieved with zero training data, and combining prior recordings with subjectspecific calibration data substantially outperforms using subject-specific data only. Our results further show that transfer between recordings under slightly different experimental setups is feasible.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Identifying Cause and Effect on Discrete Data using Additive Noise Models

Peters, J., Janzing, D., Schölkopf, B.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 597-604, (Editors: YW Teh and M Titterington), JMLR, Cambridge, MA, USA, 13th International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work we extend the notion of additive noise models to these cases. Whenever the joint distribution P(X;Y ) admits such a model in one direction, e.g. Y = f(X) + N; N ? X, it does not admit the reversed model X = g(Y ) + ~N ; ~N ? Y as long as the model is chosen in a generic way. Based on these deliberations we propose an efficient new algorithm that is able to distinguish between cause and effect for a finite sample of discrete variables. We show that this algorithm works both on synthetic and real data sets.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-supervised Learning via Generalized Maximum Entropy

Erkan, A., Altun, Y.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 209-216, (Editors: Teh, Y.W. , M. Titterington), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics , May 2010 (inproceedings)

Abstract
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally. Experimental evaluation of special cases shows the competitiveness of our methodology.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A New Algorithm for Improving the Resolution of Cryo-EM Density Maps

Hirsch, M., Schölkopf, B., Habeck, M.

In Research in Computational Molecular Biology, Lecture Notes in Bioinformatics, Vol. 6044 , pages: 174-188, (Editors: B Berger), Springer, Berlin, Germany, 14th International Conference on Research in Computational Molecular Biology (RECOMB), May 2010 (inproceedings)

Abstract
Cryo-electron microscopy (cryo-EM) plays an increasingly prominent role in structure elucidation of macromolecular assemblies. Advances in experimental instrumentation and computational power have spawned numerous cryo-EM studies of large biomolecular complexes resulting in the reconstruction of three-dimensional density maps at intermediate and low resolution. In this resolution range, identification and interpretation of structural elements and modeling of biomolecular structure with atomic detail becomes problematic. In this paper, we present a novel algorithm that enhances the resolution of intermediate- and low-resolution density maps. Our underlying assumption is to model the low-resolution density map as a blurred and possibly noise-corrupted version of an unknown high-resolution map that we seek to recover by deconvolution. By exploiting the nonnegativity of both the high-resolution map and blur kernel we derive multiplicative updates reminiscent of those used in nonnegative matrix factorization. Our framework allows for easy incorporation of additional prior knowledge such as smoothness and sparseness, on both the sharpened density map and the blur kernel. A probabilistic formulation enables us to derive updates for the hyperparameters, therefore our approach has no parameter that needs adjustment. We apply the algorithm to simulated three-dimensional electron microscopic data. We show that our method provides better resolved density maps when compared with B-factor sharpening, especially in the presence of noise. Moreover, our method can use additional information provided by homologous structures, which helps to improve the resolution even further.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Movement Templates for Learning of Hitting and Batting

Kober, J., Mülling, K., Krömer, O., Lampert, C., Schölkopf, B., Peters, J.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pages: 853-858, IEEE, Piscataway, NJ, USA, 2010 IEEE International Conference on Robotics and Automation (ICRA), May 2010 (inproceedings)

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Machine-Learning Methods for Decoding Intentional Brain States

Hill, NJ.

Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

Abstract
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user‘s initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since “it doesn‘t matter what classifier you use once your features are extracted.” Using examples from our own MEG and EEG experiments, I‘ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than “just” classification, and can be used to find better feature extractors.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis in Unsupervised Learning

Seldin, Y.

Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Experiments with Motor Primitives to learn Table Tennis

Peters, J., Mülling, K., Kober, J.

In Experimental Robotics, pages: 1-13, (Editors: Khatib, O. , V. Kumar, G. Sukhatme), Springer, Berlin, Germany, 12th International Symposium on Experimental Robotics (ISER), March 2010 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Causality: Objectives and Assessment

Guyon, I., Janzing, D., Schölkopf, B.

In JMLR Workshop and Conference Proceedings: Volume 6 , pages: 1-42, (Editors: I Guyon and D Janzing and B Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (inproceedings)

Abstract
The NIPS 2008 workshop on causality provided a forum for researchers from different horizons to share their view on causal modeling and address the difficult question of assessing causal models. There has been a vivid debate on properly separating the notion of causality from particular models such as graphical models, which have been dominating the field in the past few years. Part of the workshop was dedicated to discussing the results of a challenge, which offered a wide variety of applications of causal modeling. We have regrouped in these proceedings the best papers presented. Most lectures were videotaped or recorded. All information regarding the challenge and the lectures are found at http://www.clopinet.com/isabelle/Projects/NIPS2008/. This introduction provides a synthesis of the findings and a gentle introduction to causality topics, which are the object of active research.

ei

Web [BibTex]

Web [BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J.

EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei

[BibTex]

[BibTex]


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Learning Continuous Grasp Affordances by Sensorimotor Exploration

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J.

In From Motor Learning to Interaction Learning in Robots, pages: 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling

Kober, J., Mohler, B., Peters, J.

In From Motor Learning to Interaction Learning in Robots, pages: 209-225, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamical system-based motor primitives [Ijspeert et al(2002)Ijspeert, Nakanishi, and Schaal] that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for dynamical system-based motor primitives.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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From Motor Learning to Interaction Learning in Robots

Sigaud, O., Peters, J.

In From Motor Learning to Interaction Learning in Robots, pages: 1-12, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside wellstructured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust their behaviour to the encountered situations and required tasks. Learning approaches pose one of the most appealing ways to achieve this goal. However, while learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods from the machine learning community as these usually do not scale into the domains of robotics due to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches are needed. In this book, we focus on several core domains of robot learning. For accurate task execution, we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the most promising approach. Self improvement requires reinforcement learning approaches that scale into the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will need a form of interaction learning. This chapter provides a general introduction to these issues and briefly presents the contributions of the subsequent chapters to the corresponding research topics.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Real-Time Local GP Model Learning

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

In From Motor Learning to Interaction Learning in Robots, 264, pages: 193-207, Studies in Computational Intelligence, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
For many applications in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, D’Souza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Leveraging Sequence Classification by Taxonomy-based Multitask Learning

Widmer, C., Leiva, J., Altun, Y., Rätsch, G.

In Research in Computational Molecular Biology, LNCS, Vol. 6044, pages: 522-534, (Editors: B Berger), Springer, Berlin, Germany, 14th Annual International Conference, RECOMB, 2010 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Machine Learning Methods for Automatic Image Colorization

Charpiat, G., Bezrukov, I., Hofmann, M., Altun, Y., Schölkopf, B.

In Computational Photography: Methods and Applications, pages: 395-418, Digital Imaging and Computer Vision, (Editors: Lukac, R.), CRC Press, Boca Raton, FL, USA, 2010 (inbook)

Abstract
We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a non-uniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Probabilistic latent variable models for distinguishing between cause and effect

Mooij, J., Stegle, O., Janzing, D., Zhang, K., Schölkopf, B.

In Advances in Neural Information Processing Systems 23, pages: 1687-1695, (Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta), Curran, Red Hook, NY, USA, 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y. The basic idea is to model the observed data using probabilistic latent variable models, which incorporate the effects of unobserved noise. To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive). An important novel aspect of our work is that we do not restrict the model class, but instead put general non-parametric priors on this function and on the distribution of the cause. The causal direction can then be inferred by using standard Bayesian model selection. We evaluate our approach on synthetic data and real-world data and report encouraging results.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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JigPheno: Semantic Feature Extraction in biological images

Karaletsos, T., Stegle, O., Winn, J., Borgwardt, K.

In NIPS, Workshop on Machine Learning in Computational Biology, 2010 (inproceedings)

ei

[BibTex]

[BibTex]


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Nonparametric Tree Graphical Models

Song, L., Gretton, A., Guestrin, C.

In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Volume 9 , pages: 765-772, (Editors: YW Teh and M Titterington ), JMLR, AISTATS, 2010 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Novel machine learning methods for MHC Class I binding prediction

Widmer, C., Toussaint, N., Altun, Y., Kohlbacher, O., Rätsch, G.

In Pattern Recognition in Bioinformatics, pages: 98-109, (Editors: TMH Dijkstra and E Tsivtsivadze and E Marchiori and T Heskes), Springer, Berlin, Germany, 5th IAPR International Conference, PRIB, 2010 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Bootstrapping Apprenticeship Learning

Boularias, A., Chaib-Draa, B.

In Advances in Neural Information Processing Systems 23, pages: 289-297, (Editors: Lafferty, J. , C. K.I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta), Curran, Red Hook, NY, USA, Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
We consider the problem of apprenticeship learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is maximizing a utility function that is a linear combination of state-action features. Most IRL algorithms use a simple Monte Carlo estimation to approximate the expected feature counts under the expert's policy. In this paper, we show that the quality of the learned policies is highly sensitive to the error in estimating the feature counts. To reduce this error, we introduce a novel approach for bootstrapping the demonstration by assuming that: (i), the expert is (near-)optimal, and (ii), the dynamics of the system is known. Empirical results on gridworlds and car racing problems show that our approach is able to learn good policies from a small number of demonstrations.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models

Zhang, K., Hyvärinen, A.

In JMLR Workshop and Conference Proceedings, Volume 6, pages: 157-164, (Editors: I Guyon and D Janzing and B Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop), 2010 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition

Danafar, S., Gretton, A., Schmidhuber, J.

In Machine Learning and Knowledge Discovery in Databases, LNCS Vol. 6321, pages: 264-279, (Editors: JL Balcázar and F Bonchi and A Gionis and M Sebag), Springer, Berlin, Germany, ECML PKDD, 2010 (inproceedings)

Abstract
Embedding probability distributions into a sufficiently rich (characteristic) reproducing kernel Hilbert space enables us to take higher order statistics into account. Characterization also retains effective statistical relation between inputs and outputs in regression and classification. Recent works established conditions for characteristic kernels on groups and semigroups. Here we study characteristic kernels on periodic domains, rotation matrices, and histograms. Such structured domains are relevant for homogeneity testing, forward kinematics, forward dynamics, inverse dynamics, etc. Our kernel-based methods with tailored characteristic kernels outperform previous methods on robotics problems and also on a widely used benchmark for recognition of human actions in videos.

ei

DOI [BibTex]

DOI [BibTex]


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Movement extraction by detecting dynamics switches and repetitions

Chiappa, S., Peters, J.

In Advances in Neural Information Processing Systems 23, pages: 388-396, (Editors: Lafferty, J. , C. K.I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta), Curran, Red Hook, NY, USA, Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
Many time-series such as human movement data consist of a sequence of basic actions, e.g., forehands and backhands in tennis. Automatically extracting and characterizing such actions is an important problem for a variety of different applications. In this paper, we present a probabilistic segmentation approach in which an observed time-series is modeled as a concatenation of segments corresponding to different basic actions. Each segment is generated through a noisy transformation of one of a few hidden trajectories representing different types of movement, with possible time re-scaling. We analyze three different approximation methods for dealing with model intractability, and demonstrate how the proposed approach can successfully segment table tennis movements recorded using a robot arm as haptic input device.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Approaches Based on Support Vector Machine to Classification of Remote Sensing Data

Bruzzone, L., Persello, C.

In Handbook of Pattern Recognition and Computer Vision, pages: 329-352, (Editors: Chen, C.H.), ICP, London, UK, 2010 (inbook)

Abstract
This chapter presents an extensive and critical review on the use of kernel methods and in particular of support vector machines (SVMs) in the classification of remote-sensing (RS) data. The chapter recalls the mathematical formulation and the main theoretical concepts related to SVMs, and discusses the motivations at the basis of the use of SVMs in remote sensing. A review on the main applications of SVMs in classification of remote sensing is given, presenting a literature survey on the use of SVMs for the analysis of different kinds of RS images. In addition, the most recent methodological developments related to SVM-based classification techniques in RS are illustrated by focusing on semisupervised, domain adaptation, and context sensitive approaches. Finally, the most promising research directions on SVM in RS are identified and discussed.

ei

Web [BibTex]

Web [BibTex]


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Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

Harmeling, S., Hirsch, M., Schölkopf, B.

In Advances in Neural Information Processing Systems 23, pages: 829-837, (Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta), Curran, Red Hook, NY, USA, 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
Modelling camera shake as a space-invariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera. In an effort to address these limitations, (i) we introduce a taxonomy of camera shakes, (ii) we build on a recently introduced framework for space-variant filtering by Hirsch et al. and a fast algorithm for single image blind deconvolution for space-invariant filters by Cho and Lee to construct a method for blind deconvolution in the case of space-variant blur, and (iii), we present an experimental setup for evaluation that allows us to take images with real camera shake while at the same time recording the spacevariant point spread function corresponding to that blur. Finally, we demonstrate that our method is able to deblur images degraded by spatially-varying blur originating from real camera shake, even without using additionally motion sensor information.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Getting lost in space: Large sample analysis of the resistance distance

von Luxburg, U., Radl, A., Hein, M.

In Advances in Neural Information Processing Systems 23, pages: 2622-2630, (Editors: Lafferty, J. , C. K.I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta), Curran, Red Hook, NY, USA, Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an expression that does not take into account the structure of the graph at all and that is completely meaningless as a distance function on the graph. Consequently, the use of the raw commute distance for machine learning purposes is strongly discouraged for large graphs and in high dimensions. As an alternative we introduce the amplified commute distance that corrects for the undesired large sample effects.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Distinguishing between cause and effect

Mooij, J., Janzing, D.

In JMLR Workshop and Conference Proceedings: Volume 6, pages: 147-156, (Editors: Guyon, I. , D. Janzing, B. Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , 2010 (inproceedings)

Abstract
We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challenge #2: Pot-Luck competition. Each set consists of a sample of a pair of statistically dependent random variables. One variable is known to cause the other one, but this information was hidden from the participants; the task was to identify which of the two variables was the cause and which one the effect, based upon the observed sample. The data sets were chosen such that we expect common agreement on the ground truth. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect relation between the two variables in each pair. We also present baseline results using three different causal inference methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Methods for Detecting the Direction of Time Series

Peters, J., Janzing, D., Gretton, A., Schölkopf, B.

In Advances in Data Analysis, Data Handling and Business Intelligence, pages: 57-66, (Editors: A Fink and B Lausen and W Seidel and A Ultsch), Springer, Berlin, Germany, 32nd Annual Conference of the Gesellschaft f{\"u}r Klassifikation e.V. (GfKl), 2010 (inproceedings)

Abstract
We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finite-dimensional distributions of the time series (classification method) by embedding these distributions into a Reproducing Kernel Hilbert Space. For the ARMA method we fit the observed data with an autoregressive moving average process and test whether the regression residuals are statistically independent of the past values. Whenever the dependence in one direction is significantly weaker than in the other we infer the former to be the true one. Both approaches were able to detect the direction of the true generating model for simulated data sets. We also applied our tests to a large number of real world time series. The ARMA method made a decision for a significant fraction of them, in which it was mostly correct, while the classification method did not perform as well, but still exceeded chance level.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Switched Latent Force Models for Movement Segmentation

Alvarez, M., Peters, J., Schölkopf, B., Lawrence, N.

In Advances in neural information processing systems 23, pages: 55-63, (Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta), Curran, Red Hook, NY, USA, 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

Abstract
Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function. Each variable to be modeled is represented as the output of a differential equation and each differential equation is driven by a weighted sum of latent functions with uncertainty given by a Gaussian process prior. In this paper we consider employing the latent force model framework for the problem of determining robot motor primitives. To deal with discontinuities in the dynamical systems or the latent driving force we introduce an extension of the basic latent force model, that switches between different latent functions and potentially different dynamical systems. This creates a versatile representation for robot movements that can capture discrete changes and non-linearities in the dynamics. We give illustrative examples on both synthetic data and for striking movements recorded using a BarrettWAM robot as haptic input device. Our inspiration is robot motor primitives, but we expect our model to have wide application for dynamical systems including models for human motion capture data and systems biology.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Adhesion recovery and passive peeling in a wall climbing robot using adhesives

Kute, C., Murphy, M. P., Mengüç, Y., Sitti, M.

In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages: 2797-2802, 2010 (inproceedings)

pi

[BibTex]

[BibTex]


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Nanohandling robot cells

Fatikow, Sergej, Wich, Thomas, Dahmen, Christian, Jasper, Daniel, Stolle, Christian, Eichhorn, Volkmar, Hagemann, Saskia, Weigel-Jech, Michael

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-31, CRC Press, 2010 (incollection)

pi

[BibTex]

[BibTex]


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Locally weighted regression for control

Ting, J., Vijayakumar, S., Schaal, S.

In Encyclopedia of Machine Learning, pages: 613-624, (Editors: Sammut, C.;Webb, G. I.), Springer, 2010, clmc (inbook)

Abstract
This is article addresses two topics: learning control and locally weighted regression.

am

link (url) [BibTex]

link (url) [BibTex]


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Are reaching movements planned in kinematic or dynamic coordinates?

Ellmer, A., Schaal, S.

In Abstracts of Neural Control of Movement Conference (NCM 2010), Naples, Florida, 2010, 2010, clmc (inproceedings)

Abstract
Whether human reaching movements are planned and optimized in kinematic (task space) or dynamic (joint or muscle space) coordinates is still an issue of debate. The first hypothesis implies that a planner produces a desired end-effector position at each point in time during the reaching movement, whereas the latter hypothesis includes the dynamics of the muscular-skeletal control system to produce a continuous end-effector trajectory. Previous work by Wolpert et al (1995) showed that when subjects were led to believe that their straight reaching paths corresponded to curved paths as shown on a computer screen, participants adapted the true path of their hand such that they would visually perceive a straight line in visual space, despite that they actually produced a curved path. These results were interpreted as supporting the stance that reaching trajectories are planned in kinematic coordinates. However, this experiment could only demonstrate that adaptation to altered paths, i.e. the position of the end-effector, did occur, but not that the precise timing of end-effector position was equally planned, i.e., the trajectory. Our current experiment aims at filling this gap by explicitly testing whether position over time, i.e. velocity, is a property of reaching movements that is planned in kinematic coordinates. In the current experiment, the velocity profiles of cursor movements corresponding to the participant's hand motions were skewed either to the left or to the right; the path itself was left unaltered. We developed an adaptation paradigm, where the skew of the velocity profile was introduced gradually and participants reported no awareness of any manipulation. Preliminary results indicate that the true hand motion of participants did not alter, i.e. there was no adaptation so as to counterbalance the introduced skew. However, for some participants, peak hand velocities were lowered for higher skews, which suggests that participants interpreted the manipulation as mere noise due to variance in their own movement. In summary, for a visuomotor transformation task, the hypothesis of a planned continuous end-effector trajectory predicts adaptation to a modified velocity profile. The current experiment found no systematic adaptation under such transformation, but did demonstrate an effect that is more in accordance that subjects could not perceive the manipulation and rather interpreted as an increase of noise.

am

[BibTex]

[BibTex]


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Absence of element specific ferromagnetism in Co doped ZnO investigated by soft X-ray resonant reflectivity

Goering, E., Brück, S., Tietze, T., Jakob, G., Gacic, M., Adrian, H.

In 200, Glasgow, Scotland, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Probing the local magnetization dynamics in large systems with spatial inhomogeneity

Li, J, Lee, M.-S., Amaladass, E., He, W., Eimüller, T.

In 200, Glasgow, Scotland, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Wetting of grain boundaries in Al by the solid Al3Mg2 phase

Straumal, B. B., Baretzky, B., Kogtenkova, O. A., Straumal, A. B., Sidorenko, A. S.

In 45, pages: 2057-2061, Athens, Greek, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]