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2010


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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]

2010


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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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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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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Critical Casimir forces involving a chemically structured substrate

Parisen Toldin, F., Dietrich, S.

In International Journal of Modern Physics, 25, pages: 355-359, University of Oklahoma, USA, 2010 (inproceedings)

icm

DOI [BibTex]

DOI [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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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]


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Damping of near-adiabatic magnetization dynamics by excitations of electron-hole pairs

Seib, J., Steiauf, D., Fähnle, M.

In 200, Karlsruhe, Germany, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Comparison of linear and nonlinear buck converter models with varying compensator gain values for design optimization

Sattler, Michael, Lui, Yusi, Edrington, Chris S

In North American Power Symposium (NAPS), 2010, pages: 1-7, 2010 (inproceedings)

pi

[BibTex]

[BibTex]


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Optimality in Neuromuscular Systems

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

In 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, clmc (inproceedings)

Abstract
Abstract? We provide an overview of optimal control meth- ods to nonlinear neuromuscular systems and discuss their lim- itations. Moreover we extend current optimal control methods to their application to neuromuscular models with realistically numerous musculotendons; as most prior work is limited to torque-driven systems. Recent work on computational motor control has explored the used of control theory and esti- mation as a conceptual tool to understand the underlying computational principles of neuromuscular systems. After all, successful biological systems regularly meet conditions for stability, robustness and performance for multiple classes of complex tasks. Among a variety of proposed control theory frameworks to explain this, stochastic optimal control has become a dominant framework to the point of being a standard computational technique to reproduce kinematic trajectories of reaching movements (see [12]) In particular, we demonstrate the application of optimal control to a neuromuscular model of the index finger with all seven musculotendons producing a tapping task. Our simu- lations include 1) a muscle model that includes force- length and force-velocity characteristics; 2) an anatomically plausible biomechanical model of the index finger that includes a tendi- nous network for the extensor mechanism and 3) a contact model that is based on a nonlinear spring-damper attached at the end effector of the index finger. We demonstrate that it is feasible to apply optimal control to systems with realistically large state vectors and conclude that, while optimal control is an adequate formalism to create computational models of neuro- musculoskeletal systems, there remain important challenges and limitations that need to be considered and overcome such as contact transitions, curse of dimensionality, and constraints on states and controls.

am

PDF [BibTex]

PDF [BibTex]


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Magnetization reversal of Fe/Gd multilayers on self-assembled arrays of nanospheres

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

In 200, Glasgow, Scotland, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Enhancing the performance of Bio-inspired adhesives

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

In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 240, 2010 (inproceedings)

pi

[BibTex]

[BibTex]


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Control performance simulation in the design of a flapping wing micro-aerial vehicle

Hines, L. L., Arabagi, V., Sitti, M.

In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pages: 1090-1095, 2010 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Learning Policy Improvements with Path Integrals

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

In International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 2010, clmc (inproceedings)

Abstract
With the goal to generate more scalable algo- rithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classi- cal techniques from optimal control and dy- namic programming with modern learning techniques from statistical estimation the- ory. In this vein, this paper suggests the framework of stochastic optimal control with path integrals to derive a novel approach to RL with parametrized policies. While solidly grounded in value function estimation and optimal control based on the stochastic Hamilton-Jacobi-Bellman (HJB) equations, policy improvements can be transformed into an approximation problem of a path inte- gral which has no open parameters other than the exploration noise. The resulting algorithm can be conceived of as model- based, semi-model-based, or even model free, depending on how the learning problem is structured. Our new algorithm demon- strates interesting similarities with previous RL research in the framework of proba- bility matching and provides intuition why the slightly heuristically motivated proba- bility matching approach can actually per- form well. Empirical evaluations demon- strate significant performance improvements over gradient-based policy learning and scal- ability to high-dimensional control problems. We believe that Policy Improvement with Path Integrals (PI2) offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL based on trajectory roll-outs.

am

PDF [BibTex]

PDF [BibTex]


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Learning optimal control solutions: a path integral approach

Theodorou, E., Schaal, S.

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

Abstract
Investigating principles of human motor control in the framework of optimal control has had a long tradition in neural control of movement, and has recently experienced a new surge of investigations. Ideally, optimal control problems are addresses as a reinforcement learning (RL) problem, which would allow to investigate both the process of acquiring an optimal control solution as well as the solution itself. Unfortunately, the applicability of RL to complex neural and biomechanics systems has been largely impossible so far due to the computational difficulties that arise in high dimensional continuous state-action spaces. As a way out, research has focussed on computing optimal control solutions based on iterative optimal control methods that are based on linear and quadratic approximations of dynamical models and cost functions. These methods require perfect knowledge of the dynamics and cost functions while they are based on gradient and Newton optimization schemes. Their applicability is also restricted to low dimensional problems due to problematic convergence in high dimensions. Moreover, the process of computing the optimal solution is removed from the learning process that might be plausible in biology. In this work, we present a new reinforcement learning method for learning optimal control solutions or motor control. This method, based on the framework of stochastic optimal control with path integrals, has a very solid theoretical foundation, while resulting in surprisingly simple learning algorithms. It is also possible to apply this approach without knowledge of the system model, and to use a wide variety of complex nonlinear cost functions for optimization. We illustrate the theoretical properties of this approach and its applicability to learning motor control tasks for reaching movements and locomotion studies. We discuss its applicability to learning desired trajectories, variable stiffness control (co-contraction), and parameterized control policies. We also investigate the applicability to signal dependent noise control systems. We believe that the suggested method offers one of the easiest to use approaches to learning optimal control suggested in the literature so far, which makes it ideally suited for computational investigations of biological motor control.

am

[BibTex]

[BibTex]


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Contact angles by the solid-phase grain boundary wetting (coverage) in the Co-Cu system

Straumal, B. B., Kogtenkova, O. A., Straumal, A. B., Kuchyeyev, Y. O., Baretzky, B.

In 45, pages: 4271-4275, Glasgow, Scotland, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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A highly integrated low pressure fluid servo-valve for applications in wearable robotic systems

Folgheraiter, M., Jordan, M., Vaca Benitez, L. M., Grimminger, F., Schmidt, S., Albiez, J., Kirchner, F.

In ICINCO 2010 - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, 2, pages: 72-78, 2010 (inproceedings)

Abstract
In this paper an innovative low pressure servo-valve is presented. The device was designed with the main aim to be easily integrable into complex hydraulic/pneumatic actuation systems, and to operate at relatively low pressure (<50·105Pa). Characteristics like compactness, lightweight, high bandwidth, and autonomous sensory capability, where considered during the design process in order to achieve a device that fulfills the basic requirements for a wearable robotic system. Preliminary results about the prototype performances are presented here, in particular its dynamic behavior was measured for different working conditions, and a non-linear model identified using a recursive Hammerstein-Wiener parameter adaptation algorithm.

am

[BibTex]

[BibTex]


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Constrained Accelerations for Controlled Geometric Reduction: Sagittal-Plane Decoupling for Bipedal Locomotion

Gregg, R., Righetti, L., Buchli, J., Schaal, S.

In 2010 10th IEEE-RAS International Conference on Humanoid Robots, pages: 1-7, IEEE, Nashville, USA, 2010 (inproceedings)

Abstract
Energy-shaping control methods have produced strong theoretical results for asymptotically stable 3D bipedal dynamic walking in the literature. In particular, geometric controlled reduction exploits robot symmetries to control momentum conservation laws that decouple the sagittal-plane dynamics, which are easier to stabilize. However, the associated control laws require high-dimensional matrix inverses multiplied with complicated energy-shaping terms, often making these control theories difficult to apply to highly-redundant humanoid robots. This paper presents a first step towards the application of energy-shaping methods on real robots by casting controlled reduction into a framework of constrained accelerations for inverse dynamics control. By representing momentum conservation laws as constraints in acceleration space, we construct a general expression for desired joint accelerations that render the constraint surface invariant. By appropriately choosing an orthogonal projection, we show that the unconstrained (reduced) dynamics are decoupled from the constrained dynamics. Any acceleration-based controller can then be used to stabilize this planar subsystem, including passivity-based methods. The resulting control law is surprisingly simple and represents a practical way to employ control theoretic stability results in robotic platforms. Simulated walking of a 3D compass-gait biped show correspondence between the new and original controllers, and simulated motions of a 16-DOF humanoid demonstrate the applicability of this method.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Unusual super-ductility at room temperature in an ultrafine-grained aluminum alloy

Valiev, R. Z., Murashkin, M. Y., Kilmametov, A., Straumal, B., Chinh, N. Q., Langdon, T.

In 45, pages: 4718-4724, Seattle, WA, USA, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Demagnetization on the fs time-scale by the Elliott-Yafet mechanism

Steiauf, D., Illg, C., Fähnle, M.

In 200, Karlsruhe, Germany, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Surface tension driven water strider robot using circular footpads

Ozcan, O., Wang, H., Taylor, J. D., Sitti, M.

In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages: 3799-3804, 2010 (inproceedings)

pi

[BibTex]

[BibTex]


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Variable impedance control - a reinforcement learning approach

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

In Robotics Science and Systems (2010), Zaragoza, Spain, June 27-30, 2010, clmc (inproceedings)

Abstract
One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high DOF robotic tasks. In this contribution, we accomplish such gain scheduling with a reinforcement learning approach algorithm, PI2 (Policy Improvement with Path Integrals). PI2 is a model-free, sampling based learning method derived from first principles of optimal control. The PI2 algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PI2 is that it can scale to problems of many DOFs, so that RL on real robotic systems becomes feasible. We sketch the PI2 algorithm and its theoretical properties, and how it is applied to gain scheduling. We evaluate our approach by presenting results on two different simulated robotic systems, a 3-DOF Phantom Premium Robot and a 6-DOF Kuka Lightweight Robot. We investigate tasks where the optimal strategy requires both tuning of the impedance of the end-effector, and tuning of a reference trajectory. The results show that we can use path integral based RL not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

am

link (url) [BibTex]

link (url) [BibTex]


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The X-ray microscopy beamline UE46-PGM2 at BESSY

Follath, R., Schmidt, J. S., Weigand, M., Fauth, K.

In 10th International Conference on Synchrotron Radiation Instrumentation, 1234, pages: 323-326, AIP Conference Proceedings, American Institute of Physics, Melbourne, Australia, 2010 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Inverse dynamics with optimal distribution of ground reaction forces for legged robot

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

In Proceedings of the 13th International Conference on Climbing and Walking Robots (CLAWAR), pages: 580-587, Nagoya, Japan, sep 2010 (inproceedings)

Abstract
Contact interaction with the environment is crucial in the design of locomotion controllers for legged robots, to prevent slipping for example. Therefore, it is of great importance to be able to control the effects of the robots movements on the contact reaction forces. In this contribution, we extend a recent inverse dynamics algorithm for floating base robots to optimize the distribution of contact forces while achieving precise trajectory tracking. The resulting controller is algorithmically simple as compared to other approaches. Numerical simulations show that this result significantly increases the range of possible movements of a humanoid robot as compared to the previous inverse dynamics algorithm. We also present a simplification of the result where no inversion of the inertia matrix is needed which is particularly relevant for practical use on a real robot. Such an algorithm becomes interesting for agile locomotion of robots on difficult terrains where the contacts with the environment are critical, such as walking over rough or slippery terrain.

am mg

DOI [BibTex]

DOI [BibTex]


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Effects of Wheel Synchronization for the Hybrid Leg-Wheel Robot Asguard

Babu, A., Joyeux, S., Schwendner, J., Grimminger, F.

In Proceedings of International Symposium on Artificial Intelligence, Robotics and Automation in Space. International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS-10), August 29-September 1, Sapporo, Japan, August 2010 (inproceedings)

am

[BibTex]

[BibTex]

2009


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A computational model of human table tennis for robot application

Mülling, K., Peters, J.

In AMS 2009, pages: 57-64, (Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Zöllner, T. Gindele), Springer, Berlin, Germany, Autonome Mobile Systeme, December 2009 (inproceedings)

Abstract
Table tennis is a difficult motor skill which requires all basic components of a general motor skill learning system. In order to get a step closer to such a generic approach to the automatic acquisition and refinement of table tennis, we study table tennis from a human motor control point of view. We make use of the basic models of discrete human movement phases, virtual hitting points, and the operational timing hypothesis. Using these components, we create a computational model which is aimed at reproducing human-like behavior. We verify the functionality of this model in a physically realistic simulation of a BarrettWAM.

ei

Web DOI [BibTex]

2009


Web DOI [BibTex]


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A PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y., Tishby, N.

In Proceedings of the NIPS 2009 Workshop "Clustering: Science or Art? Towards Principled Approaches", pages: 1-4, NIPS Workshop "Clustering: Science or Art? Towards Principled Approaches", December 2009 (inproceedings)

Abstract
Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of “how many clusters are present in the data?”, and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering’s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning new basic Movements for Robotics

Kober, J., Peters, J.

In AMS 2009, pages: 105-112, (Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Zöllner, T. Gindele), Springer, Berlin, Germany, Autonome Mobile Systeme, December 2009 (inproceedings)

Abstract
Obtaining novel skills is one of the most important problems in robotics. Machine learning techniques may be a promising approach for automatic and autonomous acquisition of movement policies. However, this requires both an appropriate policy representation and suitable learning algorithms. Employing the most recent form of the dynamical systems motor primitives originally introduced by Ijspeert et al. [1], we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning, and present our current best performing learning algorithms. Finally, we show that it is possible to include a start-up phase in rhythmic primitives. We apply our approach to two elementary movements, i.e., Ball-in-a-Cup and Ball-Paddling, which can be learned on a real Barrett WAM robot arm at a pace similar to human learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Notes on Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML), December 2009 (inproceedings)

Abstract
Generalizing the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of (|V |1/3) on the approximation factor for the (s, t) cut version of the problem. On the positive side, we propose and compare three approximation algorithms with an overall approximation factor of O(min{|V |,p|E| log |V |}) that appear to do well in practice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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From Motor Learning to Interaction Learning in Robots

Sigaud, O., Peters, J.

In Proceedings of 7ème Journées Nationales de la Recherche en Robotique, pages: 189-195, JNRR, November 2009 (inproceedings)

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 well-structured 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. We focus here 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 contribution provides a general introduction to these issues and briefly presents the contributions of the related book chapters to the corresponding research topics.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval

Lampert, CH.

In ICCV 2009, pages: 987-994, IEEE Computer Society, Piscataway, NJ, USA, Twelfth IEEE International Conference on Computer Vision, October 2009 (inproceedings)

Abstract
We study the task of detecting the occurrence of objects in large image collections or in videos, a problem that combines aspects of content based image retrieval and object localization. While most previous approaches are either limited to special kinds of queries, or do not scale to large image sets, we propose a new method, efficient subimage retrieval (ESR), which is at the same time very flexible and very efficient. Relying on a two-layered branch-and-bound setup, ESR performs object-based image retrieval in sets of 100,000 or more images within seconds. An extensive evaluation on several datasets shows that ESR is not only very fast, but it also achieves detection accuracies that are on par with or superior to previously published methods for object-based image retrieval.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A new non-monotonic algorithm for PET image reconstruction

Sra, S., Kim, D., Dhillon, I., Schölkopf, B.

In IEEE - Nuclear Science Symposium Conference Record (NSS/MIC), 2009, pages: 2500-2502, (Editors: B Yu), IEEE, Piscataway, NJ, USA, IEEE Nuclear Science Symposium and Medical Imaging Conference, October 2009 (inproceedings)

Abstract
Maximizing some form of Poisson likelihood (either with or without penalization) is central to image reconstruction algorithms in emission tomography. In this paper we introduce NMML, a non-monotonic algorithm for maximum likelihood PET image reconstruction. NMML offers a simple and flexible procedure that also easily incorporates standard convex regular-ization for doing penalized likelihood estimation. A vast number image reconstruction algorithms have been developed for PET, and new ones continue to be designed. Among these, methods based on the expectation maximization (EM) and ordered-subsets (OS) framework seem to have enjoyed the greatest popularity. Our method NMML differs fundamentally from methods based on EM: i) it does not depend on the concept of optimization transfer (or surrogate functions); and ii) it is a rapidly converging nonmonotonic descent procedure. The greatest strengths of NMML, however, are its simplicity, efficiency, and scalability, which make it especially attractive for tomograph ic reconstruction. We provide a theoretical analysis NMML, and empirically observe it to outperform standard EM based methods, sometimes by orders of magnitude. NMML seamlessly allows integreation of penalties (regularizers) in the likelihood. This ability can prove to be crucial, especially because with the rapidly rising importance of combined PET/MR scanners, one will want to include more “prior” knowledge into the reconstruction.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Approximation Algorithms for Tensor Clustering

Jegelka, S., Sra, S., Banerjee, A.

In Algorithmic Learning Theory: 20th International Conference, pages: 368-383, (Editors: Gavalda, R. , G. Lugosi, T. Zeugmann, S. Zilles), Springer, Berlin, Germany, ALT, October 2009 (inproceedings)

Abstract
We present the first (to our knowledge) approximation algo- rithm for tensor clustering—a powerful generalization to basic 1D clustering. Tensors are increasingly common in modern applications dealing with complex heterogeneous data and clustering them is a fundamental tool for data analysis and pattern discovery. Akin to their 1D cousins, common tensor clustering formulations are NP-hard to optimize. But, unlike the 1D case no approximation algorithms seem to be known. We address this imbalance and build on recent co-clustering work to derive a tensor clustering algorithm with approximation guarantees, allowing metrics and divergences (e.g., Bregman) as objective functions. Therewith, we answer two open questions by Anagnostopoulos et al. (2008). Our analysis yields a constant approximation factor independent of data size; a worst-case example shows this factor to be tight for Euclidean co-clustering. However, empirically the approximation factor is observed to be conservative, so our method can also be used in practice.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Active learning using mean shift optimization for robot grasping

Kroemer, O., Detry, R., Piater, J., Peters, J.

In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pages: 2610-2615, IEEE Service Center, Piscataway, NJ, USA, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2009 (inproceedings)

Abstract
When children learn to grasp a new object, they often know several possible grasping points from observing a parent&lsquo;s demonstration and subsequently learn better grasps by trial and error. From a machine learning point of view, this process is an active learning approach. In this paper, we present a new robot learning framework for reproducing this ability in robot grasping. For doing so, we chose a straightforward approach: first, the robot observes a few good grasps by demonstration and learns a value function for these grasps using Gaussian process regression. Subsequently, it chooses grasps which are optimal with respect to this value function using a mean-shift optimization approach, and tries them out on the real system. Upon every completed trial, the value function is updated, and in the following trials it is more likely to choose even better grasping points. This method exhibits fast learning due to the data-efficiency of Gaussian process regression framework and the fact th at t he mean-shift method provides maxima of this cost function. Experiments were repeatedly carried out successfully on a real robot system. After less than sixty trials, our system has adapted its grasping policy to consistently exhibit successful grasps.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Sparse online model learning for robot control with support vector regression

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

In Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pages: 3121-3126, IEEE Service Center, Piscataway, NJ, USA, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2009 (inproceedings)

Abstract
The increasing complexity of modern robots makes it prohibitively hard to accurately model such systems as required by many applications. In such cases, machine learning methods offer a promising alternative for approximating such models using measured data. To date, high computational demands have largely restricted machine learning techniques to mostly offline applications. However, making the robots adaptive to changes in the dynamics and to cope with unexplored areas of the state space requires online learning. In this paper, we propose an approximation of the support vector regression (SVR) by sparsification based on the linear independency of training data. As a result, we obtain a method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques, such as nu-SVR, Gaussian process regression (GPR) and locally weighted projection regression (LWPR).

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Implicit Wiener Series Analysis of Epileptic Seizure Recordings

Barbero, A., Franz, M., Drongelen, W., Dorronsoro, J., Schölkopf, B., Grosse-Wentrup, M.

In EMBC 2009, pages: 5304-5307, (Editors: Y Kim and B He and G Worrell and X Pan), IEEE Service Center, Piscataway, NJ, USA, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, September 2009 (inproceedings)

Abstract
Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of nonlinearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether quadratic representations yield more accurate classifiers than linear ones. To do so we first show how to derive statistical information on the Volterra coefficient distribution and how to construct seizure classification patterns over that information. As our results illustrate, a quadratic model seems to provide no advantages over a linear one. Nevertheless, we shall also show that the interpretability of the implicit Wiener series provides insights into the inter-channel relationships of the recordings.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Incorporating Prior Knowledge on Class Probabilities into Local Similarity Measures for Intermodality Image Registration

Hofmann, M., Schölkopf, B., Bezrukov, I., Cahill, N.

In Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis , pages: 220-231, (Editors: W Wells and S Joshi and K Pohl), PMMIA, September 2009 (inproceedings)

Abstract
We present a methodology for incorporating prior knowledge on class probabilities into the registration process. By using knowledge from the imaging modality, pre-segmentations, and/or probabilistic atlases, we construct vectors of class probabilities for each image voxel. By defining new image similarity measures for distribution-valued images, we show how the class probability images can be nonrigidly registered in a variational framework. An experiment on nonrigid registration of MR and CT full-body scans illustrates that the proposed technique outperforms standard mutual information (MI) and normalized mutual information (NMI) based registration techniques when measured in terms of target registration error (TRE) of manually labeled fiducials.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Object Localization with Global and Local Context Kernels

Blaschko, M., Lampert, C.

In British Machine Vision Conference 2009, pages: 1-11, BMVC, September 2009 (inproceedings)

Abstract
Recent research has shown that the use of contextual cues significantly improves performance in sliding window type localization systems. In this work, we propose a method that incorporates both global and local context information through appropriately defined kernel functions. In particular, we make use of a weighted combination of kernels defined over local spatial regions, as well as a global context kernel. The relative importance of the context contributions is learned automatically, and the resulting discriminant function is of a form such that localization at test time can be solved efficiently using a branch and bound optimization scheme. By specifying context directly with a kernel learning approach, we achieve high localization accuracy with a simple and efficient representation. This is in contrast to other systems that incorporate context for which expensive inference needs to be done at test time. We show experimentally on the PASCAL VOC datasets that the inclusion of context can significantly improve localization performance, provided the relative contributions of context cues are learned appropriately.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Efficient Sample Reuse in EM-Based Policy Search

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

In 16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pages: 469-484, (Editors: Buntine, W. , M. Grobelnik, D. Mladenic, J. Shawe-Taylor), Springer, Berlin, Germany, ECML PKDD, September 2009 (inproceedings)

Abstract
Direct policy search is a promising reinforcement learning framework in particular for controlling in continuous, high-dimensional systems such as anthropomorphic robots. Policy search often requires a large number of samples for obtaining a stable policy update estimator due to its high flexibility. However, this is prohibitive when the sampling cost is expensive. In this paper, we extend a EM-based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, called Reward-weighted Regression with sample Reuse, is demonstrated through a robot learning experiment.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Active Structured Learning for High-Speed Object Detection

Lampert, C., Peters, J.

In DAGM 2009, pages: 221-231, (Editors: Denzler, J. , G. Notni, H. Süsse), Springer, Berlin, Germany, 31st Annual Symposium of the German Association for Pattern Recognition, September 2009 (inproceedings)

Abstract
High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more. Consecutive frames in high speed video sequences are typically very redundant, and for training an object detection system, it is sufficient to have training labels from only a subset of all images. We propose an active learning method that select training examples in a data-driven way, thereby minimizing the required number of training labeling. Experiments on realistic data show that the active learning is superior to previously used methods for dataset subsampling for this task.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Markerless 3D Face Tracking (DAGM 2009)

Walder, C., Breidt, M., Bülthoff, H., Schölkopf, B., Curio, C.

In Pattern Recognition, Lecture Notes in Computer Science, Vol. 5748 , pages: 41-50, (Editors: J Denzler and G Notni and H Süsse), Springer, Berlin, Germany, 31st Symposium of the German Association for Pattern Recognition (DAGM), September 2009 (inproceedings)

Abstract
We present a novel algorithm for the markerless tracking of deforming surfaces such as faces. We acquire a sequence of 3D scans along with color images at 40Hz. The data is then represented by implicit surface and color functions, using a novel partition-of-unity type method of efficiently combining local regressors using nearest neighbor searches. Both these functions act on the 4D space of 3D plus time, and use temporal information to handle the noise in individual scans. After interactive registration of a template mesh to the first frame, it is then automatically deformed to track the scanned surface, using the variation of both shape and color as features in a dynamic energy minimization problem. Our prototype system yields high-quality animated 3D models in correspondence, at a rate of approximately twenty seconds per timestep. Tracking results for faces and other objects are presented.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Generalized Clustering via Kernel Embeddings

Jegelka, S., Gretton, A., Schölkopf, B., Sriperumbudur, B., von Luxburg, U.

In KI 2009: AI and Automation, Lecture Notes in Computer Science, Vol. 5803, pages: 144-152, (Editors: B Mertsching and M Hund and Z Aziz), Springer, Berlin, Germany, 32nd Annual Conference on Artificial Intelligence (KI), September 2009 (inproceedings)

Abstract
We generalize traditional goals of clustering towards distinguishing components in a non-parametric mixture model. The clusters are not necessarily based on point locations, but on higher order criteria. This framework can be implemented by embedding probability distributions in a Hilbert space. The corresponding clustering objective is very general and relates to a range of common clustering concepts.

ei

PDF PDF Web DOI [BibTex]

PDF PDF Web DOI [BibTex]


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Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series

Stegle, O., Denby, KJ., Wild, DL., McHattie, S., Mead, A., Ghahramani, Z., Borgwardt, KM.

In Proceedings of the German Conference on Bioinformatics 2009 (GCB 2009), pages: 133-142, (Editors: Grosse, I. , S. Neumann, S. Posch, F. Schreiber, P. F. Stadler), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics (GCB '09), September 2009 (inproceedings)

Abstract
A wealth of time series of microarray measurements have become available over recent years. Several two-sample tests for detecting differential gene expression in these time series have been defined, but they can only answer the question whether a gene is differentially expressed across the whole time series, not in which intervals it is differentially expressed. In this article, we propose a Gaussian process based approach for studying these dynamics of differential gene expression. In experiments on Arabidopsis thaliana gene expression levels, our novel technique helps us to uncover that the family of WRKY transcription factors appears to be involved in the early response to infection by a fungal pathogen.

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PDF Web [BibTex]

PDF Web [BibTex]