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2010


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Bayesian Online Multitask Learning of Gaussian Processes

Pillonetto, G., Dinuzzo, F., De Nicolao, G.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2):193-205, February 2010 (article)

Abstract
Standard single-task kernel methods have recently been extended to the case of multitask learning in the context of regularization theory. There are experimental results, especially in biomedicine, showing the benefit of the multitask approach compared to the single-task one. However, a possible drawback is computational complexity. For instance, when regularization networks are used, complexity scales as the cube of the overall number of training data, which may be large when several tasks are involved. The aim of this paper is to derive an efficient computational scheme for an important class of multitask kernels. More precisely, a quadratic loss is assumed and each task consists of the sum of a common term and a task-specific one. Within a Bayesian setting, a recursive online algorithm is obtained, which updates both estimates and confidence intervals as new data become available. The algorithm is tested on two simulated problems and a real data set relative to xenobiotics administration in human patients.

ei

DOI [BibTex]

2010


DOI [BibTex]


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The semigroup approach to transport processes in networks

Dorn, B., Fijavz, M., Nagel, R., Radl, A.

Physica D: Nonlinear Phenomena, 239(15):1416-1421, January 2010 (article)

Abstract
We explain how operator semigroups can be used to study transport processes in networks. This method is applied to a linear Boltzmann equation on a finite as well as on an infinite network and yields well-posedness and information on the long term behavior of the solutions to the presented problems.

ei

Web DOI [BibTex]

Web DOI [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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Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design

Seeger, M., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 63(1):116-126, January 2010 (article)

Abstract
The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standard signal processing primitives, resulting in an efficient optimization algorithm for Cartesian and spiral trajectories. On clinical resolution brain image data from a Siemens 3T scanner, automatically optimized trajectories lead to significantly improved images, compared to standard low-pass, equispaced, or variable density randomized designs. Insights into the nonlinear design optimization problem for MRI are given.

ei

Web DOI [BibTex]

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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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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Consistent Nonparametric Tests of Independence

Gretton, A., Györfi, L.

Journal of Machine Learning Research, 11, pages: 1391-1423, 2010 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

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

BMC Bioinformatics, 11 Suppl 8, pages: S5, 2010 (article)

Abstract
The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm.

ei

Web DOI [BibTex]

Web DOI [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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Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)

Peters, J., Kober, J., Schaal, S.

Automatisierungstechnik, 58(12):688-694, 2010, clmc (article)

Abstract
Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.

am

link (url) [BibTex]


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Gait planning based on kinematics for a quadruped gecko model with redundancy

Son, D., Jeon, D., Nam, W. C., Chang, D., Seo, T., Kim, J.

Robotics and Autonomous Systems, 58, 2010 (article)

pi

[BibTex]

[BibTex]


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Molecular QED of coherent and incoherent sum-frequency and second-harmonic generation in chiral liquids in the presence of a static electric field

Fischer, P., Salam, A.

MOLECULAR PHYSICS, 108(14):1857-1868, 2010 (article)

Abstract
Coherent second-order nonlinear optical processes are symmetry forbidden in centrosymmetric environments in the electric-dipole approximation. In liquids that contain chiral molecules, however, and which therefore lack mirror image symmetry, coherent sum-frequency generation is possible, whereas second-harmonic generation remains forbidden. Here we apply the theory of molecular quantum electrodynamics to the calculation of the matrix element, transition rate, and integrated signal intensity for sum-frequency and second-harmonic generation taking place in a chiral liquid in the presence and absence of a static electric field, to examine which coherent and incoherent processes exist in the electric-dipole approximation in liquids. Third- and fourth-order time-dependent perturbation theory is employed in combination with single-sided Feynman diagrams to evaluate two contributions arising from static field-free and field-induced processes. It is found that, in addition to the coherent term, an incoherent process exists for sum-frequency generation in liquids. Surprisingly, in the case of dc-field-induced second-harmonic generation, the incoherent contribution is found to always vanish for isotropic chiral liquids even though hyper-Rayleigh second-harmonic generation and electric-field-induced second-harmonic generation are both independently symmetry allowed in any liquid.

pf

DOI [BibTex]


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Flat dry elastomer adhesives as attachment materials for climbing robots

Unver, O., Sitti, M.

IEEE transactions on robotics, 26(1):131-141, IEEE, 2010 (article)

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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A Bayesian approach to nonlinear parameter identification for rigid-body dynamics

Ting, J., DSouza, A., Schaal, S.

Neural Networks, 2010, clmc (article)

Abstract
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.

am

link (url) [BibTex]


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A first optimal control solution for a complex, nonlinear, tendon driven neuromuscular finger model

Theodorou, E. A., Todorov, E., Valero-Cuevas, F.

Proceedings of the ASME 2010 Summer Bioengineering Conference August 30-September 2, 2010, Naples, Florida, USA, 2010, clmc (article)

Abstract
In this work we present the first constrained stochastic op- timal feedback controller applied to a fully nonlinear, tendon driven index finger model. Our model also takes into account an extensor mechanism, and muscle force-length and force-velocity properties. We show this feedback controller is robust to noise and perturbations to the dynamics, while successfully handling the nonlinearities and high dimensionality of the system. By ex- tending prior methods, we are able to approximate physiological realism by ensuring positivity of neural commands and tendon tensions at all timesthus can, for the first time, use the optimal control framework to predict biologically plausible tendon tensions for a nonlinear neuromuscular finger model. METHODS 1 Muscle Model The rigid-body triple pendulum finger model with slightly viscous joints is actuated by Hill-type muscle models. Joint torques are generated by the seven muscles of the index fin-

am

PDF [BibTex]

PDF [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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Dzyaloshinskii-Moriya interactions in systems with fabrication induced strain gradients: ab-initio study

Beck, P., Fähnle, M

{Journal of Magnetism and Magnetic Materials}, 322, pages: 3701-3703, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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On the nature of displacement bursts during nanoindentation of ultrathin Ni films on sapphire

Rabkin, E., Deuschle, J. K., Baretzky, B.

{Acta Materialia}, 58, pages: 1589-1598, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Nanospheres generate out-of-plane magnetization

Amaladass, E., Ludescher, B., Schütz, G., Tyliszczak, T., Lee, M., Eimüller, T.

{Journal of Applied Physics}, 107, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Temperature dependence of the magnetic properties of L10-FePt nanostructures and films

Bublat, T., Goll, D.

{Journal of Applied Physics}, 108(11), 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic properties of Fe nanoclusters on Cu(111) studied with X-ray magnetic circular dichroism

Fauth, K., Ballentine, G., Praetorius, C., Kleibert, A., Wilken, N., Voitkans, A., Meiwes-Broer, K.-H.

{Physica Status Solidi B}, 247(5):1170-1179, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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An experimental analysis of elliptical adhesive contact

Sümer, B., Onal, C. D., Aksak, B., Sitti, M.

Journal of Applied Physics, 107(11):113512, AIP, 2010 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Efficient learning and feature detection in high dimensional regression

Ting, J., D’Souza, A., Vijayakumar, S., Schaal, S.

Neural Computation, 22, pages: 831-886, 2010, clmc (article)

Abstract
We present a novel algorithm for efficient learning and feature selection in high- dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the Expectation- Maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This Variational Bayesian Least Squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust â??black- boxâ? approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector Machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques.

am

link (url) [BibTex]

link (url) [BibTex]


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Nanoscale imaging using deep ultraviolet digital holographic microscopy

Faridian, A., Hopp, D., Pedrini, G., Eigenthaler, U., Hirscher, M., Osten, W.

{Optics Express}, 18(13):14159-14164, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Metal-organic frameworks for hydrogen storage

Hirscher, M., Panella, B., Schmitz, B.

{Microporous and Mesoporous Materials}, 129, pages: 335-339, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Samarium-cobalt 2:17 magnets: analysis of the coercive field of Sm2(CoFeCuZr)17 high-temperature permanent magnets

Goll, D., Stadelmaier, H. H., Kronmüller, H.

{Scripta Materialia}, 63, pages: 243-245, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Low-temperature growth of silicon nanotubes and nanowires on amorphous substrates

Mbenkum, B. N., Schneider, A. S., Schütz, G., Xu, C., Richter, G., van Aken, P. A., Majer, G., Spatz, J. P.

{ACS Nano}, 4(4):1805-1812, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Physisorption in porous materials

Hirscher, M., Panella, B.

In Handbook of Hydrogen Storage, pages: 39-62, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2010 (incollection)

mms

[BibTex]

[BibTex]


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Formation and mobility of protonic charge carriers in methyl sulfonic acid-water mixtures: A model for sulfonic acid based ionomers at low degree of hydration

Telfah, A., Majer, G., Kreuer, K. D., Schuster, M., Maier, J.

{Solid State Ionics}, 181, pages: 461-465, 2010 (article)

mms

[BibTex]

[BibTex]


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Continuous photobleaching to study the growth modes of focal adhesions

de Beer, A. G. F., Majer, G., Roke, S., Spatz, J. P.

{Journal of Adhesion Science and Technology}, 24, pages: 2323-2334, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic antivortex-core reversal by circular-rotational spin currents

Kamionka, T., Martens, M., Chou, K. W., Curcic, M., Drews, A., Schütz, G., Tyliszczak, T., Stoll, H., Van Waeyenberge, B., Meier, G.

{Physical Review Letters}, 105, 2010 (article)

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DOI [BibTex]

DOI [BibTex]


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Extension of Yafet\textquotesingles theory of spin relaxation to ferromagnets

Steiauf, D., Illg, C., Fähnle, M.

{Journal of Magnetism and Magnetic Materials}, 322, pages: L5-L7, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Stochastic Differential Dynamic Programming

Theodorou, E., Tassa, Y., Todorov, E.

In the proceedings of American Control Conference (ACC 2010) , 2010, clmc (article)

Abstract
We present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state- and control-dependent process noise, and proceed to derive the second-order expansion of the cost-to-go. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

am

PDF [BibTex]

PDF [BibTex]


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Enhanced wet adhesion and shear of elastomeric micro-fiber arrays with mushroom tip geometry and a photopolymerized p (DMA-co-MEA) tip coating

Glass, P., Chung, H., Washburn, N. R., Sitti, M.

Langmuir, 26(22):17357-17362, American Chemical Society, 2010 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Lateral transport of thermal capillary waves

Smith, T. H. R., Vasilyev, O., Maciolek, A., Schmidt, M.

{Europhysics Letters}, 89(1), 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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The formation and propagation of flux avalanches in tailored MgB2 films

Treiber, S., Albrecht, J.

{New Journal of Physics}, 12, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Direct imaging of current induced magnetic vortex gyration in an asymmetric potential well

Bisig, A., Rhensius, J., Kammerer, M., Curcic, M., Stoll, H., Schütz, G., Van Waeyenberge, B., Chou, K. W., Tyliszczak, T., Heyderman, L. J., Krzyk, S., von Bieren, A., Kläui, M.

{Applied Physics Letters}, 96, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Induced magnetism of carbon atoms at the graphene/Ni(111) interface

Weser, M., Rehder, Y., Horn, K., Sicot, M., Fonin, M., Preobrajenski, A. B., Voloshina, E. N., Goering, E., Dedkov, Y. S.

{Applied Physics Letters}, 96, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Photon counting system for time-resolved experiments in multibunch mode

Puzic, A., Korhonen, T., Kalantari, B., Raabe, J., Quitmann, C., Jüllig, P., Bommer, L., Goll, D., Schütz, G., Wintz, S., Strache, T., Körner, M., Markó, D., Bunce, C., Fassbender, J.

{Synchrotron Radiation News}, 23(2):26-32, 2010 (article)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Coupling of Fe and uncompensated Mn moments in exchange-biased Fe/MnPd

Brück, S., Macke, S., Goering, E., Ji, X., Zhan, Q., Krishnan, K. M.

{Physical Review B}, 81(13), 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Remarks about spillover and hydrogen adsorption - Comments on the contributions of A.V. Talyzin and R.T. Yang

Hirscher, M.

{Microporous and Mesoporous Materials}, 135, pages: 209-210, 2010 (article)

mms

DOI [BibTex]


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Grain boundary ridges and triple lines

Straumal, B. B., Sursaeva, V. G., Baretzky, B.

{Scripta Materialia}, 62(12):924-927, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Expanding micelle nanolithography to the self-assembly of multicomponent core-shell nanoparticles

Mbenkum, B. N., D\’\iaz-Ortiz, A., Gu, L., van Aken, P. A., Schütz, G.

{Journal of the American Chemical Society}, 132(31):10671-10673, 2010 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Learning control in robotics – trajectory-based opitimal control techniques

Schaal, S., Atkeson, C. G.

Robotics and Automation Magazine, 17(2):20-29, 2010, clmc (article)

Abstract
In a not too distant future, robots will be a natural part of daily life in human society, providing assistance in many areas ranging from clinical applications, education and care giving, to normal household environments [1]. It is hard to imagine that all possible tasks can be preprogrammed in such robots. Robots need to be able to learn, either by themselves or with the help of human supervision. Additionally, wear and tear on robots in daily use needs to be automatically compensated for, which requires a form of continuous self-calibration, another form of learning. Finally, robots need to react to stochastic and dynamic environments, i.e., they need to learn how to optimally adapt to uncertainty and unforeseen changes. Robot learning is going to be a key ingredient for the future of autonomous robots. While robot learning covers a rather large field, from learning to perceive, to plan, to make decisions, etc., we will focus this review on topics of learning control, in particular, as it is concerned with learning control in simulated or actual physical robots. In general, learning control refers to the process of acquiring a control strategy for a particular control system and a particular task by trial and error. Learning control is usually distinguished from adaptive control [2] in that the learning system can have rather general optimization objectivesâ??not just, e.g., minimal tracking errorâ??and is permitted to fail during the process of learning, while adaptive control emphasizes fast convergence without failure. Thus, learning control resembles the way that humans and animals acquire new movement strategies, while adaptive control is a special case of learning control that fulfills stringent performance constraints, e.g., as needed in life-critical systems like airplanes. Learning control has been an active topic of research for at least three decades. However, given the lack of working robots that actually use learning components, more work needs to be done before robot learning will make it beyond the laboratory environment. This article will survey some ongoing and past activities in robot learning to assess where the field stands and where it is going. We will largely focus on nonwheeled robots and less on topics of state estimation, as typically explored in wheeled robots [3]â??6], and we emphasize learning in continuous state-action spaces rather than discrete state-action spaces [7], [8]. We will illustrate the different topics of robot learning with examples from our own research with anthropomorphic and humanoid robots.

am

link (url) [BibTex]

link (url) [BibTex]


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Learning, planning, and control for quadruped locomotion over challenging terrain

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

International Journal of Robotics Research, 30(2):236-258, 2010, clmc (article)

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 terrains of varying difficulty levels. The terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by an independent external test team on terrain that has never been shown to us.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Teleoperated 3-D force feedback from the nanoscale with an atomic force microscope

Onal, C. D., Sitti, M.

IEEE Transactions on nanotechnology, 9(1):46-54, IEEE, 2010 (article)

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[BibTex]

[BibTex]