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2013


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Enhanced fabrication and characterization of gecko-inspired mushroom-tipped microfiber adhesives

Song, J., Mengüç, Y., Sitti, M.

Journal of Adhesion Science and Technology, 27(17):1921-1932, Routledge, 2013 (article)

pi

Project Page [BibTex]

2013


Project Page [BibTex]


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Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis

Zahedi, K., Martius, G., Ay, N.

Frontiers in Psychology, 4(801), 2013 (article)

Abstract
One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost.

al

link (url) DOI [BibTex]


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Switching modes in easy and hard axis magnetic reversal in a self-assembled antidot array

Haering, F., Wiedwald, U., Nothelfer, S., Koslowski, B., Ziemann, P., Lechner, L., Wallucks, A., Lebecki, K., Nowak, U., Gräfe, J., Goering, E., Schütz, G.

{Nanotechnology}, 24, IOP Pub., Bristol, UK, 2013 (article)

mms

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Time-resolved imaging of nonlinear magnetic domain-wall dynamics in ferromagnetic nanowires

Stein, F.-U., Bocklage, L., Weigand, M., Meier, G.

{Scientific Reports}, 3, Nature Publishing Group, London, UK, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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A cryogenically flexible covalent organic framework for efficient hydrogen isotrope separation by quantum sieving

Oh, H., Kalidindi, S. B., Um, Y., Bureekaew, S., Schmid, R., Fischer, R. A., Hirscher, M.

{Angewandte Chemie International Edition in English}, 52(50):13219-13222, Wiley-VCH Verlag GmbH & Co. KGaA, D-69451 Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unexpected room-temperature ferromagnetism in bulk ZnO

Chen, Y., Goering, E., Jeurgens, L., Wang, Z., Phillipp, F., Baier, J., Tietze, T., Schütz, G.

{Applied Physics Letters}, (103), American Institute of Physics, Melville, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Large-area hard magnetic L10-FePt and composite L10-FePt based nanopatterns

Goll, D., Bublat, T.

{Physica Status Solidi A-Applications and Materials Science}, 210(7):1261-1271, Wiley-VCH, Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Wave modes of collective vortex gyration in dipolar-coupled-dot-array magnonic crystals

Han, D., Vogel, A., Jung, H., Lee, K., Weigand, M., Stoll, H., Schütz, G., Fischer, P., Meier, G., Kim, S.

{Scientific Reports}, 3, Nature Publishing Group, London, UK, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

ei

link (url) [BibTex]

link (url) [BibTex]


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A Perching Mechanism for Flying Robots Using a Fibre-Based Adhesive

Daler, L., Klaptocz, A., Briod, A., Sitti, M., Floreano, D.

In Robotics and Automation (ICRA), 2013 IEEE International Conference on, 2013 (inproceedings)

pi

[BibTex]

[BibTex]


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Micro-scale mobile robotics

Diller, E., Sitti, M.

Foundations and Trends in Robotics, 2(3):143-259, Now Publishers Incorporated, 2013 (article)

pi

[BibTex]

[BibTex]


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Survey and Introduction to the Focused Section on Bio-Inspired Mechatronics

Sitti, M., Menciassi, A., Ijspeert, A., Low, K. H., Kim, S.

Mechatronics, IEEE/ASME Transactions on, 18(2):409-418, DOI: 10.1109/TMECH.2012. 2233492, 2013 (article)

pi

[BibTex]

[BibTex]


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Bonding methods for modular micro-robotic assemblies

Diller, E., Zhang, N., Sitti, M.

In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages: 2588-2593, 2013 (inproceedings)

pi

[BibTex]

[BibTex]


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Robustness of guided self-organization against sensorimotor disruptions

Martius, G.

Advances in Complex Systems, 16(02n03):1350001, 2013 (article)

Abstract
Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g.~to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

al

DOI [BibTex]

DOI [BibTex]


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Controlled Reduction with Unactuated Cyclic Variables: Application to 3D Bipedal Walking with Passive Yaw Rotation

Gregg, R., Righetti, L.

IEEE Transactions on Automatic Control, 58(10):2679-2685, October 2013 (article)

Abstract
This technical note shows that viscous damping can shape momentum conservation laws in a manner that stabilizes yaw rotation and enables steering for underactuated 3D walking. We first show that unactuated cyclic variables can be controlled by passively shaped conservation laws given a stabilizing controller in the actuated coordinates. We then exploit this result to realize controlled geometric reduction with multiple unactuated cyclic variables. We apply this underactuated control strategy to a five-link 3D biped to produce exponentially stable straight-ahead walking and steering in the presence of passive yawing.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Task Error Models for Manipulation

Pastor, P., Kalakrishnan, M., Binney, J., Kelly, J., Righetti, L., Sukhatme, G. S., Schaal, S.

In 2013 IEEE Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, 2013 (inproceedings)

Abstract
Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

ei

link (url) [BibTex]

link (url) [BibTex]


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Ferromagnetism of zinc oxide nanograined films

Straumal, B. B., Protasova, S. G., Mazilkin, A. A., Schütz, G., Goering, E., Baretzky, B., Straumal, P. B.

{Journal of Experimental and Theoretical Physics Letters}, 97(6):367-377, Pleiades Publishing, Inc., 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Hydrogen adsorption properties of platinum decorated hierarchically structured templated carbons

Oh, H., Gennett, T., Atanassov, P., Kurttepeli, M., Bals, S., Hurst, K. E., Hirscher, M.

{Microporous and Mesoporous Materials}, pages: 66-74, Elsevier, Amsterdam, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Extended s-d models for the dynamics of noncollinear magnetization: Short review of two different approaches

Fähnle, M., Zhang, S.

{Journal of Magnetism and Magnetic Materials}, 326, pages: 232-234, NH, Elsevier, Amsterdam, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Correlation between spin structure oscillations and domain wall velocities

Bisig, A., Stärk, M., Mawass, M., Moutafis, C., Rhensius, J., Heidler, J., Büttner, F., Noske, M., Weigand, M., Eisebitt, S., Tyliszczak, T., Van Wayenberge, B., Stoll, H., Schütz, G., Kläui, M.

{Nature Communications}, 4, Nature Publishing Group, London, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Recent advances in use of atomic layer deposition and focused ion beams for fabrication of Fresnel zone plates for hard x-rays

Keskinbora, K., Robisch, A., Mayer, M., Grévent, C., Szeghalmi, A. V., Knez, M., Weigand, M., Snigireva, I., Snigirev, A., Salditt, T., Schütz, G.

{Proceedings of SPIE (The International Society for Optical Engineering)}, 8851, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic states in low-pinning high-anisotropy material nanostructures suitable for dynamic imaging

Büttner, F., Moutafis, C., Bisig, A., Wohlhüter, P., Günther, C. M., Mohanty, J., Geilhufe, J., Schneider, M., v. Korff Schmising, C., Schaffert, S., Pfau, B., Hantschmann, M., Riemeier, M., Emmel, M., Finizio, S., Jakob, G., Weigand, M., Rhensius, J., Franken, J. H., Lavrijsen, R., Swagten, H. J. M., Stoll, H., Eisebitt, S., Kläui, M.

{Physical Review B}, 87, Published by the American Physical Society through the American Institute of Physics, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Experimental and theoretical study of D2/H2 quantum sieving in a carbon molecular sieve

Gotzias, A., Charalambopoulou, G., Ampoumogli, A., Krkljus, I., Hirscher, M., Steriotis, T.

{Adsorption}, 19(2-4):373-379, Springer Science+Business Media, New York, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


Non-parametric hand pose estimation with object context
Non-parametric hand pose estimation with object context

Romero, J., Kjellström, H., Ek, C. H., Kragic, D.

Image and Vision Computing , 31(8):555 - 564, 2013 (article)

Abstract
In the spirit of recent work on contextual recognition and estimation, we present a method for estimating the pose of human hands, employing information about the shape of the object in the hand. Despite the fact that most applications of human hand tracking involve grasping and manipulation of objects, the majority of methods in the literature assume a free hand, isolated from the surrounding environment. Occlusion of the hand from grasped objects does in fact often pose a severe challenge to the estimation of hand pose. In the presented method, object occlusion is not only compensated for, it contributes to the pose estimation in a contextual fashion; this without an explicit model of object shape. Our hand tracking method is non-parametric, performing a nearest neighbor search in a large database (.. entries) of hand poses with and without grasped objects. The system that operates in real time, is robust to self occlusions, object occlusions and segmentation errors, and provides full hand pose reconstruction from monocular video. Temporal consistency in hand pose is taken into account, without explicitly tracking the hand in the high-dim pose space. Experiments show the non-parametric method to outperform other state of the art regression methods, while operating at a significantly lower computational cost than comparable model-based hand tracking methods.

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Publisher site pdf link (url) [BibTex]

Publisher site pdf link (url) [BibTex]

2010


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Reinforcement learning of full-body humanoid motor skills

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

In Humanoid Robots (Humanoids), 2010 10th IEEE-RAS International Conference on, pages: 405-410, December 2010, clmc (inproceedings)

Abstract
Applying reinforcement learning to humanoid robots is challenging because humanoids have a large number of degrees of freedom and state and action spaces are continuous. Thus, most reinforcement learning algorithms would become computationally infeasible and require a prohibitive amount of trials to explore such high-dimensional spaces. In this paper, we present a probabilistic reinforcement learning approach, which is derived from the framework of stochastic optimal control and path integrals. The algorithm, called Policy Improvement with Path Integrals (PI2), has a surprisingly simple form, has no open tuning parameters besides the exploration noise, is model-free, and performs numerically robustly in high dimensional learning problems. We demonstrate how PI2 is able to learn full-body motor skills on a 34-DOF humanoid robot. To demonstrate the generality of our approach, we also apply PI2 in the context of variable impedance control, where both planned trajectories and gain schedules for each joint are optimized simultaneously.

am

link (url) [BibTex]

2010


link (url) [BibTex]


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Learning Table Tennis with a Mixture of Motor Primitives

Mülling, K., Kober, J., Peters, J.

In Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010), pages: 411-416, IEEE, Piscataway, NJ, USA, 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), December 2010 (inproceedings)

Abstract
Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate control, and online adaptation. To represent the elementary movements needed for robot table tennis, we rely on dynamic systems motor primitives (DMP). While such DMPs have been successfully used for learning a variety of simple motor tasks, they only represent single elementary actions. In order to select and generalize among different striking movements, we present a new approach, called Mixture of Motor Primitives that uses a gating network to activate appropriate motor primitives. The resulting policy enables us to select among the appropriate motor primitives as well as to generalize between them. In order to obtain a fully learned robot table tennis setup, we also address the problem of predicting the necessary context information, i.e., the hitting point in time and space where we want to hit the ball. We show that the resulting setup was capable of playing rudimentary table tennis using an anthropomorphic robot arm.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Learning an interactive segmentation system

Nickisch, H., Rother, C., Kohli, P., Rhemann, C.

In Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010), pages: 274-281, (Editors: Chellapa, R. , P. Anandan, A. N. Rajagopalan, P. J. Narayanan, P. Torr), ACM Press, Nw York, NY, USA, Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), December 2010 (inproceedings)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user -- a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy

Bangert, M., Hennig, P., Oelfke, U.

In pages: 746-751 , (Editors: Draghici, S. , T.M. Khoshgoftaar, V. Palade, W. Pedrycz, M.A. Wani, X. Zhu), IEEE, Piscataway, NJ, USA, Ninth International Conference on Machine Learning and Applications (ICMLA), December 2010 (inproceedings)

Abstract
We present a method for fully automated selection of treatment beam ensembles for external radiation therapy. We reformulate the beam angle selection problem as a clustering problem of locally ideal beam orientations distributed on the unit sphere. For this purpose we construct an infinite mixture of von Mises-Fisher distributions, which is suited in general for density estimation from data on the D-dimensional sphere. Using a nonparametric Dirichlet process prior, our model infers probability distributions over both the number of clusters and their parameter values. We describe an efficient Markov chain Monte Carlo inference algorithm for posterior inference from experimental data in this model. The performance of the suggested beam angle selection framework is illustrated for one intra-cranial, pancreas, and prostate case each. The infinite von Mises-Fisher mixture model (iMFMM) creates between 18 and 32 clusters, depending on the patient anatomy. This suggests to use the iMFMM directly for beam ensemble selection in robotic radio surgery, or to generate low-dimensional input for both subsequent optimization of trajectories for arc therapy and beam ensemble selection for conventional radiation therapy.

ei pn

Web DOI [BibTex]

Web DOI [BibTex]


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Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Besserve, M., Schölkopf, B., Logothetis, N., Panzeri, S.

Journal of Computational Neuroscience, 29(3):547-566, December 2010 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach

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

SIAM Journal on Scientific Computing, 32(6):3548-3563 , December 2010 (article)

Abstract
Numerous scientific applications across a variety of fields depend on box-constrained convex optimization. Box-constrained problems therefore continue to attract research interest. We address box-constrained (strictly convex) problems by deriving two new quasi-Newton algorithms. Our algorithms are positioned between the projected-gradient [J. B. Rosen, J. SIAM, 8 (1960), pp. 181–217] and projected-Newton [D. P. Bertsekas, SIAM J. Control Optim., 20 (1982), pp. 221–246] methods. We also prove their convergence under a simple Armijo step-size rule. We provide experimental results for two particular box-constrained problems: nonnegative least squares (NNLS), and nonnegative Kullback–Leibler (NNKL) minimization. For both NNLS and NNKL our algorithms perform competitively as compared to well-established methods on medium-sized problems; for larger problems our approach frequently outperforms the competition.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Algorithmen zum Automatischen Erlernen von Motorfähigkeiten

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

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

Abstract
Robot learning methods which allow autonomous 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 fulfill 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 approach 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 algorithms 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 structures for task representation and execution.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Online algorithms for submodular minimization with combinatorial constraints

Jegelka, S., Bilmes, J.

In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning: Structures, Algorithms and Applications (DISCML), December 2010 (inproceedings)

Abstract
Building on recent results for submodular minimization with combinatorial constraints, and on online submodular minimization, we address online approximation algorithms for submodular minimization with combinatorial constraints. We discuss two types of algorithms and outline approximation algorithms that integrate into those.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis of Co-clustering and Beyond

Seldin, Y., Tishby, N.

Journal of Machine Learning Research, 11, pages: 3595-3646, December 2010 (article)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Multi-agent random walks for local clustering

Alamgir, M., von Luxburg, U.

In Proceedings of the IEEE International Conference on Data Mining (ICDM 2010), pages: 18-27, (Editors: Webb, G. I., B. Liu, C. Zhang, D. Gunopulos, X. Wu), IEEE, Piscataway, NJ, USA, IEEE International Conference on Data Mining (ICDM), December 2010 (inproceedings)

Abstract
We consider the problem of local graph clustering where the aim is to discover the local cluster corresponding to a point of interest. The most popular algorithms to solve this problem start a random walk at the point of interest and let it run until some stopping criterion is met. The vertices visited are then considered the local cluster. We suggest a more powerful alternative, the multi-agent random walk. It consists of several “agents” connected by a fixed rope of length l. All agents move independently like a standard random walk on the graph, but they are constrained to have distance at most l from each other. The main insight is that for several agents it is harder to simultaneously travel over the bottleneck of a graph than for just one agent. Hence, the multi-agent random walk has less tendency to mistakenly merge two different clusters than the original random walk. In our paper we analyze the multi-agent random walk theoretically and compare it experimentally to the major local graph clustering algorithms from the literature. We find that our multi-agent random walk consistently outperforms these algorithms.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Effects of Packet Losses to Stability in Bilateral Teleoperation Systems

Hong, A., Cho, JH., Lee, DY.

In pages: 1043-1044, Korean Society of Mechanical Engineers, Seoul, South Korea, KSME Fall Annual Meeting, November 2010 (inproceedings)

ei

[BibTex]

[BibTex]


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Gaussian Processes for Machine Learning (GPML) Toolbox

Rasmussen, C., Nickisch, H.

Journal of Machine Learning Research, 11, pages: 3011-3015, November 2010 (article)

Abstract
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace's method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

ei

Web [BibTex]

Web [BibTex]


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Combining Real-Time Brain-Computer Interfacing and Robot Control for Stroke Rehabilitation

Gomez Rodriguez, M., Peters, J., Hill, J., Gharabaghi, A., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of SIMPAR 2010 Workshops, pages: 59-63, Brain-Computer Interface Workshop at SIMPAR: 2nd International Conference on Simulation, Modeling, and Programming for Autonomous Robots, November 2010 (inproceedings)

Abstract
Brain-Computer Interfaces based on electrocorticography (ECoG) or electroencephalography (EEG), in combination with robot-assisted active physical therapy, may support traditional rehabilitation procedures for patients with severe motor impairment due to cerebrovascular brain damage caused by stroke. In this short report, we briefly review the state-of-the art in this exciting new field, give an overview of the work carried out at the Max Planck Institute for Biological Cybernetics and the University of T{\"u}bingen, and discuss challenges that need to be addressed in order to move from basic research to clinical studies.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Cryo-EM structure and rRNA model of a translating eukaryotic 80S ribosome at 5.5-Å resolution

Armache, J-P., Jarasch, A., Anger, AM., Villa, E., Becker, T., Bhushan, S., Jossinet, F., Habeck, M., Dindar, G., Franckenberg, S., Marquez, V., Mielke, T., Thomm, M., Berninghausen, O., Beatrix, B., Söding, J., Westhof, E., Wilson, DN., Beckmann, R.

Proceedings of the National Academy of Sciences of the United States of America, 107(46):19748-19753, November 2010 (article)

Abstract
Protein biosynthesis, the translation of the genetic code into polypeptides, occurs on ribonucleoprotein particles called ribosomes. Although X-ray structures of bacterial ribosomes are available, high-resolution structures of eukaryotic 80S ribosomes are lacking. Using cryoelectron microscopy and single-particle reconstruction, we have determined the structure of a translating plant (Triticum aestivum) 80S ribosome at 5.5-Å resolution. This map, together with a 6.1-Å map of a Saccharomyces cerevisiae 80S ribosome, has enabled us to model ∼98% of the rRNA. Accurate assignment of the rRNA expansion segments (ES) and variable regions has revealed unique ES–ES and r-protein–ES interactions, providing insight into the structure and evolution of the eukaryotic ribosome.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Policy gradient methods

Peters, J.

Scholarpedia, 5(11):3698, November 2010 (article)

Abstract
Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Localization of eukaryote-specific ribosomal proteins in a 5.5-Å cryo-EM map of the 80S eukaryotic ribosome

Armache, J-P., Jarasch, A., Anger, AM., Villa, E., Becker, T., Bhushan, S., Jossinet, F., Habeck, M., Dindar, G., Franckenberg, S., Marquez, V., Mielke, T., Thomm, M., Berninghausen, O., Beatrix, B., Söding, J., Westhof, E., Wilson, DN., Beckmann, R.

Proceedings of the National Academy of Sciences of the United States of America, 107(46):19754-19759, November 2010 (article)

Abstract
Protein synthesis in all living organisms occurs on ribonucleoprotein particles, called ribosomes. Despite the universality of this process, eukaryotic ribosomes are significantly larger in size than their bacterial counterparts due in part to the presence of 80 r proteins rather than 54 in bacteria. Using cryoelectron microscopy reconstructions of a translating plant (Triticum aestivum) 80S ribosome at 5.5-Å resolution, together with a 6.1-Å map of a translating Saccharomyces cerevisiae 80S ribosome, we have localized and modeled 74/80 (92.5%) of the ribosomal proteins, encompassing 12 archaeal/eukaryote-specific small subunit proteins as well as the complete complement of the ribosomal proteins of the eukaryotic large subunit. Near-complete atomic models of the 80S ribosome provide insights into the structure, function, and evolution of the eukaryotic translational apparatus.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

Camps-Valls, G., Shervashidze, N., Borgwardt, K.

IEEE Geoscience and Remote Sensing Letters, 7(4):741-745, October 2010 (article)

Abstract
This letter presents a graph kernel for spatio-spectral remote sensing image classification with support vector machines (SVMs). The method considers higher order relations in the neighborhood (beyond pairwise spatial relations) to iteratively compute a kernel matrix for SVM learning. The proposed kernel is easy to compute and constitutes a powerful alternative to existing approaches. The capabilities of the method are illustrated in several multi- and hyperspectral remote sensing images acquired over both urban and agricultural areas.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Causal Inference Using the Algorithmic Markov Condition

Janzing, D., Schölkopf, B.

IEEE Transactions on Information Theory, 56(10):5168-5194, October 2010 (article)

Abstract
Inferring the causal structure that links $n$ observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when the sample size is one. We develop a theory how to generate causal graphs explaining similarities between single objects. To this end, we replace the notion of conditional stochastic independence in the causal Markov condition with the vanishing of conditional algorithmic mutual information and describe the corresponding causal inference rules. We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs. This insight provides a theoretical foundation of a heuristic principle proposed in earlier work. We also sketch some ideas on how to replace Kolmogorov complexity with decidable complexity criteria. This can be seen as an algorithmic analog of replacing the empirically undecidable question of statistical independence with practical independence tests that are based on implicit or explicit assumptions on the underlying distribution.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Recurrent Policy Gradients

Wierstra, D., Förster, A., Peters, J., Schmidhuber, J.

Logic Journal of the IGPL, 18(5):620-634, October 2010 (article)

Abstract
Reinforcement learning for partially observable Markov decision problems (POMDPs) is a challenge as it requires policies with an internal state. Traditional approaches suffer significantly from this shortcoming and usually make strong assumptions on the problem domain such as perfect system models, state-estimators and a Markovian hidden system. Recurrent neural networks (RNNs) offer a natural framework for dealing with policy learning using hidden state and require only few limiting assumptions. As they can be trained well using gradient descent, they are suited for policy gradient approaches. In this paper, we present a policy gradient method, the Recurrent Policy Gradient which constitutes a model-free reinforcement learning method. It is aimed at training limited-memory stochastic policies on problems which require long-term memories of past observations. The approach involves approximating a policy gradient for a recurrent neural network by backpropagating return-weighted characteristic eligibilities through time. Using a ‘‘Long Short-Term Memory’’ RNN architecture, we are able to outperform previous RL methods on three important benchmark tasks. Furthermore, we show that using history-dependent baselines helps reducing estimation variance significantly, thus enabling our approach to tackle more challenging, highly stochastic environments.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning as a key ability for Human-Friendly Robots

Peters, J., Kober, J., Mülling, K., Krömer, O., Nguyen-Tuong, D., Wang, Z., Rodriguez Gomez, M., Grosse-Wentrup, M.

In pages: 1-2, 3rd Workshop for Young Researchers on Human-Friendly Robotics (HFR), October 2010 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.

In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pages: 121-126, IEEE, Piscataway, NJ, USA, IEEE International Conference on Systems, Man and Cybernetics (SMC), October 2010 (inproceedings)

Abstract
Brain-Computer Interfaces (BCIs) in combination with robot-assisted physical therapy may become a valuable tool for neurorehabilitation of patients with severe hemiparetic syndromes due to cerebrovascular brain damage (stroke) and other neurological conditions. A key aspect of this approach is reestablishing the disrupted sensorimotor feedback loop, i.e., determining the intended movement using a BCI and helping a human with impaired motor function to move the arm using a robot. It has not been studied yet, however, how artificially closing the sensorimotor feedback loop affects the BCI decoding performance. In this article, we investigate this issue in six healthy subjects, and present evidence that haptic feedback facilitates the decoding of arm movement intention. The results provide evidence of the feasibility of future rehabilitative efforts combining robot-assisted physical therapy with BCIs.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., Peters, J.

In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1586-1591, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

Abstract
This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical grasp configurations from visual descriptors that are associated with the contours of an object. While these hypothetical configurations are abundant, labeled configurations are very scarce as these are acquired via time-costly experiments carried out by the robot. Kernel logistic regression (KLR) via joint kernel maps is trained to map the hypothesis space of grasps into continuous class-conditional probability values indicating their achievability. We propose a soft-supervised extension of KLR and a framework to combine the merits of semi-supervised and active learning approaches to tackle the scarcity of labeled grasps. Experimental evaluation shows that combining active and semi-supervised learning is favorable in the existence of an oracle. Furthermore, semi-supervised learning outperforms supervised learning, particularly when the labeled data is very limited.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Discriminative frequent subgraph mining with optimality guarantees

Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H., Smola, A., Song, L., Yu, P., Yan, X., Borgwardt, K.

Journal of Statistical Analysis and Data Mining, 3(5):302–318, October 2010 (article)

Abstract
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A biomimetic approach to robot table tennis

Mülling, K., Kober, J., Peters, J.

In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), pages: 1921-1926, IEEE, Piscataway, NJ, USA, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010 (inproceedings)

Abstract
Although human beings see and move slower than table tennis or baseball robots, they manage to outperform such robot systems. One important aspect of this better performance is the human movement generation. In this paper, we study trajectory generation for table tennis from a biomimetic point of view. Our focus lies on generating efficient stroke movements capable of mastering variations in the environmental conditions, such as changing ball speed, spin and position. We study table tennis from a human motor control point of view. To make headway towards this goal, we construct a trajectory generator for a single stroke using the discrete movement stages hypothesis and the virtual hitting point hypothesis to create a model that produces a human-like stroke movement. We verify the functionality of the trajectory generator for a single forehand stroke both in a simulation and using a real Barrett WAM.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Combining active learning and reactive control for robot grasping

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

Robotics and Autonomous Systems, 58(9):1105-1116, September 2010 (article)

Abstract
Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp’s location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller’s upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshapin g the hand depending on the object’s geometry. The system was evaluated both in simulation and on a real robot.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]