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2012


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Abstract
Four decades after their invention, quasi- Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

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website+code pdf link (url) [BibTex]

2012


website+code pdf link (url) [BibTex]


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Entropy Search for Information-Efficient Global Optimization

Hennig, P., Schuler, C.

Journal of Machine Learning Research, 13, pages: 1809-1837, -, June 2012 (article)

Abstract
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.

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PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


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Learning Tracking Control with Forward Models

Bócsi, B., Hennig, P., Csató, L., Peters, J.

In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract
Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

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Web [BibTex]

Web [BibTex]


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Kernel Topic Models

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

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

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PDF Web [BibTex]

PDF Web [BibTex]


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Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps

Stueckler, J., Behnke, S.

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2012 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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RoboCup@Home: Demonstrating Everyday Manipulation Skills in RoboCup@Home

Stueckler, J., Holz, D., Behnke, S.

IEEE Robotics and Automation Magazine (RAM), 19(2):34-42, 2012 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Towards Robust Mobility, Flexible Object Manipulation, and Intuitive Multimodal Interaction for Domestic Service Robots

Stueckler, J., Droeschel, D., Gräve, K., Holz, D., Kläß, J., Schreiber, M., Steffens, R., Behnke, S.

In RoboCup 2011, Robot Soccer World Cup XV, pages: 51-62, Springer, 2012 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Bayesian calibration of the hand-eye kinematics of an anthropomorphic robot

Hubert, U., Stueckler, J., Behnke, S.

In Proc. of the 12th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 618-624, November 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Shape-Primitive Based Object Recognition and Grasping

Nieuwenhuisen, M., Stueckler, J., Berner, A., Klein, R., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Semantic mapping using object-class segmentation of RGB-D images

Stueckler, J., Biresev, N., Behnke, S.

In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages: 3005-3010, October 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Efficient Mobile Robot Navigation using 3D Surfel Grid Maps

Kläß, J., Stueckler, J., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Integrating depth and color cues for dense multi-resolution scene mapping using RGB-D cameras

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), pages: 162-167, sep 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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SURE: Surface Entropy for Distinctive 3D Features

Fiolka, T., Stueckler, J., Klein, D. A., Schulz, D., Behnke, S.

In Proc. of Spatial Cognition, 2012 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Robust Real-Time Registration of RGB-D Images using Multi-Resolution Surfel Representations

Stueckler, J., Behnke, S.

In Proc. of ROBOTIK, VDE-Verlag, 2012 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Adjustable autonomy for mobile teleoperation of personal service robots

Muszynski, S., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Symp. on Robot and Human Interactive Communication, pages: 933-940, sep 2012 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Adaptive Multi-cue 3D Tracking of Arbitrary Objects

Garcia, G. M., Klein, D. A., Stueckler, J., Frintrop, S., Cremers, A. B.

In DAGM/OAGM Symposium, 7476, pages: 357-366, Lecture Notes in Computer Science, Springer, 2012 (inproceedings)

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[BibTex]

[BibTex]

2009


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Bayesian Quadratic Reinforcement Learning

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

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

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PDF Web [BibTex]

2009


PDF Web [BibTex]


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Expectation Propagation on the Maximum of Correlated Normal Variables

Hennig, P.

Cavendish Laboratory: University of Cambridge, July 2009 (techreport)

Abstract
Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corresponding to Gaussian approximations minimizing relative entropy). It is shown how this can be used to build a heuristic approximation to the maximum relationship over a finite set of Gaussian variables, allowing approximate inference by Expectation Propagation on such quantities.

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Web [BibTex]

Web [BibTex]


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Integrating indoor mobility, object manipulation, and intuitive interaction for domestic service tasks

Stueckler, J., Behnke, S.

In Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 506-513, December 2009 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Dynamaid, an Anthropomorphic Robot for Research on Domestic Service Applications

Stueckler, J., Schreiber, M., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 87-92, 2009 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

2008


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Hierarchical Reactive Control for Humanoid Soccer Robots

Behnke, S., Stueckler, J.

International Journal of Humanoid Robots (IJHR), 5(3):375-396, 2008 (article)

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link (url) [BibTex]

2008


link (url) [BibTex]


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In-lane Localization in Road Networks using Curbs Detected in Omnidirectional Height Images

Stueckler, J., Schulz, H., Behnke, S.

In Proceedings of Robotik 2008, 2008 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Orthogonal wall correction for visual motion estimation

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 1-6, May 2008 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]