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2003


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Predictive control with Gaussian process models

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Likar, B.

In Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool, pages: 352-356, (Editors: Zajc, B. and M. Tkal), Proceedings of IEEE Region 8 Eurocon: Computer as a Tool, 2003 (inproceedings)

Abstract
This paper describes model-based predictive control based on Gaussian processes.Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

ei

PDF PostScript [BibTex]

2003


PDF PostScript [BibTex]


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Distance-based classification with Lipschitz functions

von Luxburg, U., Bousquet, O.

In Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. Our approach leads to a general large margin algorithm for classification in metric spaces. To analyze this algorithm, we first prove a representer theorem. It states that there exists a solution which can be expressed as linear combination of distances to sets of training points. Then we analyze the Rademacher complexity of some Lipschitz function classes. The generality of the Lipschitz approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz algorithm, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Semi-Supervised Learning through Principal Directions Estimation

Chapelle, O., Schölkopf, B., Weston, J.

In ICML Workshop, The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, pages: 7, ICML Workshop: The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, 2003 (inproceedings)

Abstract
We describe methods for taking into account unlabeled data in the training of a kernel-based classifier, such as a Support Vector Machines (SVM). We propose two approaches utilizing unlabeled points in the vicinity of labeled ones. Both of the approaches effectively modify the metric of the pattern space, either by using non-spherical Gaussian density estimates which are determined using EM, or by modifying the kernel function using displacement vectors computed from pairs of unlabeled and labeled points. The latter is linked to techniques for training invariant SVMs. We present experimental results indicating that the proposed technique can lead to substantial improvements of classification accuracy.

ei

PostScript [BibTex]

PostScript [BibTex]


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Machine Learning with Hyperkernels

Ong, CS., Smola, AJ.

In pages: 568-575, 2003 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals

Rasmussen, CE.

In Bayesian Statistics 7, pages: 651-659, (Editors: J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West), Bayesian Statistics 7, 2003 (inproceedings)

Abstract
Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of evaluations of the (un-normalized) posterior and its partial derivatives. In situations where the posterior is computationally costly to evaluate, this may lead to an unacceptable computational load for HMC. I propose to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC. Within this scheme only occasional evaluation of the actual posterior is required to guarantee that the samples generated have exactly the desired distribution, even if the GP model is somewhat inaccurate. The method is demonstrated on a 10 dimensional problem, where 200 evaluations suffice for the generation of 100 roughly independent points from the posterior. Thus, the proposed scheme allows Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Dimension Reduction Based on Orthogonality — a Decorrelation Method in ICA

Zhang, K., Chan, L.

In Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, pages: 132-139, (Editors: O Kaynak and E Alpaydin and E Oja and L Xu), Springer, Berlin, Germany, International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP, 2003, Lecture Notes in Computer Science, Volume 2714 (inproceedings)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Bayesian backfitting

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

In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003), Irvine, CA, May 2003, 2003, clmc (inproceedings)

Abstract
We present an algorithm aimed at addressing both computational and analytical intractability of Bayesian regression models which operate in very high-dimensional, usually underconstrained spaces. Several domains of research frequently provide such datasets, including chemometrics [2], and human movement analysis [1]. The literature in nonparametric statistics provides interesting solutions such as Backfitting [3] and Partial Least Squares [4], which are extremely robust and efficient, yet lack a probabilistic interpretation that could place them in the context of current research in statistical learning algorithms that emphasize the estimation of confidence, posterior distributions, and model complexity. In order to achieve numerical robustness and low computational cost, we first derive a novel Bayesian interpretation of Backfitting (BB) as a computationally efficient regression algorithm. BBÕs learning complexity scales linearly with the input dimensionality by decoupling inference among individual input dimensions. We embed BB in an efficient, locally variational model selection mechanism that automatically grows the number of backfitting experts in a mixture-of-experts regression model. We demonstrate the effectiveness of the algorithm in performing principled regularization of model complexity when fitting nonlinear manifolds while avoiding the numerical hazards associated with highly underconstrained problems. We also note that this algorithm appears applicable in various areas of neural computation, e.g., in abstract models of computational neuroscience, or implementations of statistical learning on artificial systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Reinforcement learning for humanoid robotics

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

In IEEE-RAS International Conference on Humanoid Robots (Humanoids2003), Karlsruhe, Germany, Sept.29-30, 2003, clmc (inproceedings)

Abstract
Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, `vanilla' policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. `Vanilla' policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade (2002) is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high dimensionally continuous state-action systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Accuracy of manual spike sorting: Results for the Utah intracortical array

Wood, F., Fellows, M., Vargas-Irwin, C., Black, M. J., Donoghue, J. P.

Program No. 279.2. 2003, Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2003, Online (conference)

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abstract [BibTex]

abstract [BibTex]


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Specular flow and the perception of surface reflectance

Roth, S., Domini, F., Black, M. J.

Journal of Vision, 3 (9): 413a, 2003 (conference)

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abstract poster [BibTex]

abstract poster [BibTex]


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Grain boundary phase transitions in the Al-Mg system and their influence on high-strain rate superplasticity

Straumal, B. B., Lopez, G. A., Mittemeijer, E. J., Gust, W., Zhilyaev, A. P.

In 216-217, pages: 307-312, Moscow, Russia, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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High aspect ratio polymer micro/nano-structure manufacturing using nanoembossing, nanomolding and directed self-assembly

Sitti, M.

In ASME 2003 International Mechanical Engineering Congress and Exposition, pages: 293-297, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Nsf workshop on future directions in nano-scale systems, dynamics and control

Sitti, M.

In Automatic Control Conference (ACC), 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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3-D nano-fiber manufacturing by controlled pulling of liquid polymers using nano-probes

Nain, A. S., Sitti, M.

In Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on, 1, pages: 60-63, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Discovering imitation strategies through categorization of multi-cimensional data

Billard, A., Epars, Y., Schaal, S., Cheng, G.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, NV, Oct. 27-31, 2003, clmc (inproceedings)

Abstract
An essential problem of imitation is that of determining Ówhat to imitateÓ, i.e. to determine which of the many features of the demonstration are relevant to the task and which should be reproduced. The strategy followed by the imitator can be modeled as a hierarchical optimization system, which minimizes the discrepancy between two multidimensional datasets. We consider imitation of a manipulation task. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different manipulation tasks and controls task reproduction by a full body humanoid robot. or the complete path followed by the demonstrator. We follow a similar taxonomy and apply it to the learning and reproduction of a manipulation task by a humanoid robot. We take the perspective that the features of the movements to imitate are those that appear most frequently, i.e. the invariants in time. The model builds upon previous work [3], [4] and is composed of a hierarchical time delay neural network that extracts invariant features from a manipulation task performed by a human demonstrator. The system analyzes the Carthesian trajectories of the objects and the joint

am

link (url) [BibTex]

link (url) [BibTex]


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Scaling reinforcement learning paradigms for motor learning

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

In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003), Irvine, CA, May 2003, 2003, clmc (inproceedings)

Abstract
Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation Ð a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that KakadeÕs Ôaverage natural policy gradientÕ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Influence of grain boundary phase transitions on the diffusion-related properties

Straumal, B., Baretzky, B.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, pages: 53-64, Defect and Diffusion Forum, Scitec Publications Ltd., Moscow, Russia, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Are carbon nanostructures an efficient hydrogen storage medium?

Hirscher, M., Becher, M., Haluska, M., von Zeppelin, F., Chen, X., Dettlaff-Weglikowska, U., Roth, S.

In 356-357, pages: 433-437, Annecy, France, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Learning attractor landscapes for learning motor primitives

Ijspeert, A., Nakanishi, J., Schaal, S.

In Advances in Neural Information Processing Systems 15, pages: 1547-1554, (Editors: Becker, S.;Thrun, S.;Obermayer, K.), Cambridge, MA: MIT Press, 2003, clmc (inproceedings)

Abstract
If globally high dimensional data has locally only low dimensional distributions, it is advantageous to perform a local dimensionality reduction before further processing the data. In this paper we examine several techniques for local dimensionality reduction in the context of locally weighted linear regression. As possible candidates, we derive local versions of factor analysis regression, principle component regression, principle component regression on joint distributions, and partial least squares regression. After outlining the statistical bases of these methods, we perform Monte Carlo simulations to evaluate their robustness with respect to violations of their statistical assumptions. One surprising outcome is that locally weighted partial least squares regression offers the best average results, thus outperforming even factor analysis, the theoretically most appealing of our candidate techniques.Ê

am

link (url) [BibTex]

link (url) [BibTex]


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Manufacturing of two and three-dimensional micro/nanostructures by integrating optical tweezers with chemical assembly

Castelino, K., Satyanarayana, S., Sitti, M.

In Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on, 1, pages: 56-59, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Grain boundary faceting phase transition and thermal grooving in Cu

Straumal, B. B., Polyakov, S. A., Bischoff, E., Mittemeijer, E. J., Gust, W.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, 216/217, pages: 93-100, Diffusion and Defect Data, Pt. A, Defect and Diffusion Forum, Scitec Publ., Moscow, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Evolution of Fault-tolerant Self-replicating Structures

Righetti, L., Shokur, S., Capcarre, M.

In Advances in Artificial Life, pages: 278-288, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2003 (inproceedings)

Abstract
Designed and evolved self-replicating structures in cellular automata have been extensively studied in the past as models of Artificial Life. However, CAs, unlike their biological counterpart, are very brittle: any faulty cell usually leads to the complete destruction of any emerging structures, let alone self-replicating structures. A way to design fault-tolerant structures based on error-correcting-code has been presented recently [1], but it required a cumbersome work to be put into practice. In this paper, we get back to the original inspiration for these works, nature, and propose a way to evolve self-replicating structures, faults here being only an idiosyncracy of the environment.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning from demonstration and adaptation of biped locomotion with dynamical movement primitives

Nakanishi, J., Morimoto, J., Endo, G., Schaal, S., Kawato, M.

In Workshop on Robot Learning by Demonstration, IEEE International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, NV, Oct. 27-31, 2003, clmc (inproceedings)

Abstract
In this paper, we report on our research for learning biped locomotion from human demonstration. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a CPG of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through the movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of oscillators. Numerical simulations demonstrate the effectiveness of the proposed locomotion controller.

am

link (url) [BibTex]

link (url) [BibTex]


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Movement planning and imitation by shaping nonlinear attractors

Schaal, S.

In Proceedings of the 12th Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, 2003, clmc (inproceedings)

Abstract
Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dy-namically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach, and, moreover, created a new foundation for imitation learning. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control and imitation in robotics and biology.

am

link (url) [BibTex]

link (url) [BibTex]


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Synthetic gecko foot-hair micro/nano-structures for future wall-climbing robots

Sitti, M., Fearing, R. S.

In Robotics and Automation, 2003. Proceedings. ICRA’03. IEEE International Conference on, 1, pages: 1164-1170, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Grain boundary faceting phase transition and thermal grooving in Cu

Straumal, B. B., Polyakov, S. A., Bischoff, E., Mittemeijer, E. J., Gust, W.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, 216/217, pages: 93-100, Diffusion and Defect Data, Pt. A, Defect and Diffusion Forum, Scitec Publ., Moscow, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Biomimetic propulsion for a swimming surgical micro-robot

Edd, J., Payen, S., Rubinsky, B., Stoller, M. L., Sitti, M.

In Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, 3, pages: 2583-2588, 2003 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Coercivity mechanism in nanocrystalline and bonded magnets

Goll, D., Kronmüller, H.

In Bonded Magnets. Proceedings of the NATO Advanced Research Workshop on Science and Technology of Bonded Magnets, 118, pages: 115-127, NATO Science Series: Series 2, Mathematics, Physics and Chemistry, Kluwer Acad. Publ., Newark, USA, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Investigation of Electromigration in Copper Interconnects by Noise Measurements

Emelianov, V., Ganesan, G., Puzic, A., Schulz, S., Eizenberg, M., Habermeier, H., Stoll, H.

In Noise as a Tool for Studying Materials, pages: 271-281, Proceedings of SPIE, Santa Fe, New Mexico, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


Thumb xl attractiveteaser
Attractive people: Assembling loose-limbed models using non-parametric belief propagation

Sigal, L., Isard, M. I., Sigelman, B. H., Black, M. J.

In Advances in Neural Information Processing Systems 16, NIPS, pages: 1539-1546, (Editors: S. Thrun and L. K. Saul and B. Schölkopf), MIT Press, 2003 (inproceedings)

Abstract
The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body models. To cope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. We formulate the pose estimation problem as one of probabilistic inference over a graphical model where the random variables correspond to the individual limb parameters (position and orientation). Because the limbs are described by 6-dimensional vectors encoding pose in 3-space, discretization is impractical and the random variables in our model must be continuous-valued. To approximate belief propagation in such a graph we exploit a recently introduced generalization of the particle filter. This framework facilitates the automatic initialization of the body-model from low level cues and is robust to occlusion of body parts and scene clutter.

ps

pdf (color) pdf (black and white) [BibTex]

pdf (color) pdf (black and white) [BibTex]


Thumb xl bildschirmfoto 2013 01 15 um 09.48.31
Neural decoding of cursor motion using a Kalman filter

(Nominated: Best student paper)

Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., Shaikhouni, A., Donoghue, J. P.

In Advances in Neural Information Processing Systems 15, pages: 133-140, MIT Press, 2003 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]

1997


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The view-graph approach to visual navigation and spatial memory

Mallot, H., Franz, M., Schölkopf, B., Bülthoff, H.

In Artificial Neural Networks: ICANN ’97, pages: 751-756, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks, October 1997 (inproceedings)

Abstract
This paper describes a purely visual navigation scheme based on two elementary mechanisms (piloting and guidance) and a graph structure combining individual navigation steps controlled by these mechanisms. In robot experiments in real environments, both mechanisms have been tested, piloting in an open environment and guidance in a maze with restricted movement opportunities. The results indicate that navigation and path planning can be brought about with these simple mechanisms. We argue that the graph of local views (snapshots) is a general and biologically plausible means of representing space and integrating the various mechanisms of map behaviour.

ei

PDF PDF DOI [BibTex]

1997


PDF PDF DOI [BibTex]


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Predicting time series with support vector machines

Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.

In Artificial Neural Networks: ICANN’97, pages: 999-1004, (Editors: Schölkopf, B. , C.J.C. Burges, A.J. Smola), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks , October 1997 (inproceedings)

Abstract
Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29%.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Predicting time series with support vectur machines

Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.

In Artificial neural networks: ICANN ’97, pages: 999-1004, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks , October 1997 (inproceedings)

Abstract
Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29%.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Kernel principal component analysis

Schölkopf, B., Smola, A., Müller, K.

In Artificial neural networks: ICANN ’97, LNCS, vol. 1327, pages: 583-588, (Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud), Springer, Berlin, Germany, 7th International Conference on Artificial Neural Networks, October 1997 (inproceedings)

Abstract
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


Thumb xl sharpening
Robust anisotropic diffusion and sharpening of scalar and vector images

Black, M. J., Sapiro, G., Marimont, D., Heeger, D.

In Int. Conf. on Image Processing, ICIP, 1, pages: 263-266, Vol. 1, Santa Barbara, CA, October 1997 (inproceedings)

Abstract
Relations between anisotropic diffusion and robust statistics are described. We show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new "edge-stopping" function based on Tukey's biweight robust estimator, that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in the image. We extend the framework to vector-valued images and show applications to robust image sharpening.

ps

pdf publisher site [BibTex]

pdf publisher site [BibTex]


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Homing by parameterized scene matching

Franz, M., Schölkopf, B., Bülthoff, H.

In Proceedings of the 4th European Conference on Artificial Life, (Eds.) P. Husbands, I. Harvey. MIT Press, Cambridge 1997, pages: 236-245, (Editors: P Husbands and I Harvey), MIT Press, Cambridge, MA, USA, 4th European Conference on Artificial Life (ECAL97), July 1997 (inproceedings)

Abstract
In visual homing tasks, animals as well as robots can compute their movements from the current view and a snapshot taken at a home position. Solving this problem exactly would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. We propose a homing scheme that dispenses with accurate distance information by using parameterized disparity fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that the approximation does not prevent the scheme from approaching the goal with arbitrary accuracy. Mobile robot experiments are used to demonstrate the practical feasibility of the approach.

ei

PDF [BibTex]

PDF [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.31.38
Robust anisotropic diffusion: Connections between robust statistics, line processing, and anisotropic diffusion

Black, M. J., Sapiro, G., Marimont, D., Heeger, D.

In Scale-Space Theory in Computer Vision, Scale-Space’97, pages: 323-326, LNCS 1252, Springer Verlag, Utrecht, the Netherlands, July 1997 (inproceedings)

ps

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.05.56
Learning parameterized models of image motion

Black, M. J., Yacoob, Y., Jepson, A. D., Fleet, D. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-97, pages: 561-567, Puerto Rico, June 1997 (inproceedings)

Abstract
A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.

ps

pdf [BibTex]

pdf [BibTex]


Thumb xl bildschirmfoto 2013 01 14 um 10.13.51
Analysis of gesture and action in technical talks for video indexing

Ju, S. X., Black, M. J., Minneman, S., Kimber, D.

In IEEE Conf. on Computer Vision and Pattern Recognition, pages: 595-601, CVPR-97, Puerto Rico, June 1997 (inproceedings)

Abstract
In this paper, we present an automatic system for analyzing and annotating video sequences of technical talks. Our method uses a robust motion estimation technique to detect key frames and segment the video sequence into subsequences containing a single overhead slide. The subsequences are stabilized to remove motion that occurs when the speaker adjusts their slides. Any changes remaining between frames in the stabilized sequences may be due to speaker gestures such as pointing or writing and we use active contours to automatically track these potential gestures. Given the constrained domain we define a simple ``vocabulary'' of actions which can easily be recognized based on the active contour shape and motion. The recognized actions provide a rich annotation of the sequence that can be used to access a condensed version of the talk from a web page.

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pdf [BibTex]

pdf [BibTex]


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Improving the accuracy and speed of support vector learning machines

Burges, C., Schölkopf, B.

In Advances in Neural Information Processing Systems 9, pages: 375-381, (Editors: M Mozer and MJ Jordan and T Petsche), MIT Press, Cambridge, MA, USA, Tenth Annual Conference on Neural Information Processing Systems (NIPS), May 1997 (inproceedings)

Abstract
Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for illposed problems . Against this very general backdrop any methods for improving the generalization performance, or for improving the speed in test phase of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem The method for improving generalization performance the "virtual support vector" method does so by incorporating known invariances of the problem This method achieves a drop in the error rate on 10.000 NIST test digit images of 1,4 % to 1 %. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance achieving 1,1 % error . The virtual support vector method is applicable to any SVM problem with known invariances The reduced set method is applicable to any support vector machine .

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Modeling appearance change in image sequences

Black, M. J., Yacoob, Y., Fleet, D. J.

In Advances in Visual Form Analysis, pages: 11-20, Proceedings of the Third International Workshop on Visual Form, Capri, Italy, May 1997 (inproceedings)

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abstract [BibTex]

abstract [BibTex]


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Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Georg, P., Mallot, H., Bülthoff, H.

In Proceedings of the 1st Intl. Conf. on Autonomous Agents, pages: 138-147, (Editors: Johnson, W.L.), ACM Press, New York, NY, USA, First International Conference on Autonomous Agents (AGENTS '97), Febuary 1997 (inproceedings)

Abstract
We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning from demonstration

Schaal, S.

In Advances in Neural Information Processing Systems 9, pages: 1040-1046, (Editors: Mozer, M. C.;Jordan, M.;Petsche, T.), MIT Press, Cambridge, MA, 1997, clmc (inproceedings)

Abstract
By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstrations of other humans. For learning control, this paper investigates how learning from demonstration can be applied in the context of reinforcement learning. We consider priming the Q-function, the value function, the policy, and the model of the task dynamics as possible areas where demonstrations can speed up learning. In general nonlinear learning problems, only model-based reinforcement learning shows significant speed-up after a demonstration, while in the special case of linear quadratic regulator (LQR) problems, all methods profit from the demonstration. In an implementation of pole balancing on a complex anthropomorphic robot arm, we demonstrate that, when facing the complexities of real signal processing, model-based reinforcement learning offers the most robustness for LQR problems. Using the suggested methods, the robot learns pole balancing in just a single trial after a 30 second long demonstration of the human instructor. 

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link (url) [BibTex]

link (url) [BibTex]


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Robot learning from demonstration

Atkeson, C. G., Schaal, S.

In Machine Learning: Proceedings of the Fourteenth International Conference (ICML ’97), pages: 12-20, (Editors: Fisher Jr., D. H.), Morgan Kaufmann, Nashville, TN, July 8-12, 1997, 1997, clmc (inproceedings)

Abstract
The goal of robot learning from demonstration is to have a robot learn from watching a demonstration of the task to be performed. In our approach to learning from demonstration the robot learns a reward function from the demonstration and a task model from repeated attempts to perform the task. A policy is computed based on the learned reward function and task model. Lessons learned from an implementation on an anthropomorphic robot arm using a pendulum swing up task include 1) simply mimicking demonstrated motions is not adequate to perform this task, 2) a task planner can use a learned model and reward function to compute an appropriate policy, 3) this model-based planning process supports rapid learning, 4) both parametric and nonparametric models can be learned and used, and 5) incorporating a task level direct learning component, which is non-model-based, in addition to the model-based planner, is useful in compensating for structural modeling errors and slow model learning. 

am

link (url) [BibTex]

link (url) [BibTex]


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Local dimensionality reduction for locally weighted learning

Vijayakumar, S., Schaal, S.

In International Conference on Computational Intelligence in Robotics and Automation, pages: 220-225, Monteray, CA, July10-11, 1997, 1997, clmc (inproceedings)

Abstract
Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as biological movement systems. So far, due to sparsity of data in high dimensional spaces, learning in such settings requires a significant amount of prior knowledge about the learning task, usually provided by a human expert. In this paper we suggest a partial revision of the view. Based on empirical studies, it can been observed that, despite being globally high dimensional and sparse, data distributions from physical movement systems are locally low dimensional and dense. Under this assumption, we derive a learning algorithm, Locally Adaptive Subspace Regression, that exploits this property by combining a local dimensionality reduction as a preprocessing step with a nonparametric learning technique, locally weighted regression. The usefulness of the algorithm and the validity of its assumptions are illustrated for a synthetic data set and data of the inverse dynamics of an actual 7 degree-of-freedom anthropomorphic robot arm.

am

link (url) [BibTex]

link (url) [BibTex]


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Learning tasks from a single demonstration

Atkeson, C. G., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA97), 2, pages: 1706-1712, Piscataway, NJ: IEEE, Albuquerque, NM, 20-25 April, 1997, clmc (inproceedings)

Abstract
Learning a complex dynamic robot manoeuvre from a single human demonstration is difficult. This paper explores an approach to learning from demonstration based on learning an optimization criterion from the demonstration and a task model from repeated attempts to perform the task, and using the learned criterion and model to compute an appropriate robot movement. A preliminary version of the approach has been implemented on an anthropomorphic robot arm using a pendulum swing up task as an example

am

link (url) [BibTex]

link (url) [BibTex]