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2018


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Nanoscale robotic agents in biological fluids and tissues

Palagi, S., Walker, D. Q. T., Fischer, P.

In The Encyclopedia of Medical Robotics, 2, pages: 19-42, 2, (Editors: Desai, J. P. and Ferreira, A.), World Scientific, October 2018 (inbook)

Abstract
Nanorobots are untethered structures of sub-micron size that can be controlled in a non-trivial way. Such nanoscale robotic agents are envisioned to revolutionize medicine by enabling minimally invasive diagnostic and therapeutic procedures. To be useful, nanorobots must be operated in complex biological fluids and tissues, which are often difficult to penetrate. In this chapter, we first discuss potential medical applications of motile nanorobots. We briefly present the challenges related to swimming at such small scales and we survey the rheological properties of some biological fluids and tissues. We then review recent experimental results in the development of nanorobots and in particular their design, fabrication, actuation, and propulsion in complex biological fluids and tissues. Recent work shows that their nanoscale dimension is a clear asset for operation in biological tissues, since many biological tissues consist of networks of macromolecules that prevent the passage of larger micron-scale structures, but contain dynamic pores through which nanorobots can move.

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link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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Haptics and Haptic Interfaces

Kuchenbecker, K. J.

In Encyclopedia of Robotics, (Editors: Marcelo H. Ang and Oussama Khatib and Bruno Siciliano), Springer, May 2018 (incollection)

Abstract
Haptics is an interdisciplinary field that seeks to both understand and engineer touch-based interaction. Although a wide range of systems and applications are being investigated, haptics researchers often concentrate on perception and manipulation through the human hand. A haptic interface is a mechatronic system that modulates the physical interaction between a human and his or her tangible surroundings. Haptic interfaces typically involve mechanical, electrical, and computational layers that work together to sense user motions or forces, quickly process these inputs with other information, and physically respond by actuating elements of the user’s surroundings, thereby enabling him or her to act on and feel a remote and/or virtual environment.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Maschinelles Lernen: Entwicklung ohne Grenzen?

Schökopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

ei

[BibTex]

[BibTex]


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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

ei

[BibTex]

[BibTex]


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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

ei

Project Page [BibTex]

Project Page [BibTex]

2012


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Exploiting pedestrian interaction via global optimization and social behaviors

Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.

In Theoretic Foundations of Computer Vision: Outdoor and Large-Scale Real-World Scene Analysis, Springer, April 2012 (incollection)

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pdf [BibTex]

2012


pdf [BibTex]


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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

ei

PDF [BibTex]

PDF [BibTex]


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Active Learning Methods in Classification of Remote Sensing Images

Bruzzone, L., Persello, C., Demir, B.

In Signal and Image Processing for Remote Sensing, (Editors: CH Chen), CRC Press, Boca Raton, FL, USA, January 2012 (inbook) In press

ei

[BibTex]

[BibTex]


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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

ei

[BibTex]

[BibTex]


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Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

ei

Web [BibTex]

Web [BibTex]


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Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract
As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Higher-Order Tensors in Diffusion MRI

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

ei

[BibTex]

[BibTex]


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Data-driven Manifolds for Outdoor Motion Capture

Pons-Moll, G., Leal-Taix’e, L., Gall, J., Rosenhahn, B.

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 305-328, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

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video publisher's site pdf Project Page [BibTex]

video publisher's site pdf Project Page [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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Automated Tip-Based 2-D Mechanical Assembly of Micro/Nanoparticles

Onal, C. D., Ozcan, O., Sitti, M.

In Feedback Control of MEMS to Atoms, pages: 69-108, Springer US, 2012 (incollection)

pi

[BibTex]

[BibTex]


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Scan-Based Flow Modelling in Human Upper Airways

Perumal Nithiarasu, Igor Sazonov, Si Yong Yeo

In Patient-Specific Modeling in Tomorrow’s Medicine, pages: 241 - 280, 0, (Editors: Amit Gefen), Springer, 2012 (inbook)

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[BibTex]

[BibTex]


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The principles of XMCD and its application to L-edges in transition metals

Schütz, G.

In Linear and Chiral Dichroism in the Electron Miroscope, pages: 23-42, Pan Stanford Publishing Pte.Ltd., Singapore, 2012 (incollection)

mms

[BibTex]

[BibTex]


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An Introduction to Random Forests for Multi-class Object Detection

Gall, J., Razavi, N., van Gool, L.

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 243-263, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

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code code for Hough forest publisher's site pdf Project Page [BibTex]

code code for Hough forest publisher's site pdf Project Page [BibTex]


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Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M. J.

In Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pages: 99-118, 6, (Editors: Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige), Springer-Verlag, 2012 (incollection)

ps

Project Page [BibTex]

Project Page [BibTex]


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Structural and chemical characterization on the nanoscale

Stierle, A., Carstanjen, H.-D., Hofmann, S.

In Nanoelectronics and Information Technology. Advanced Electronic Materials and Novel Devices, pages: 233-254, Wiley-VCH, Weinheim, 2012 (incollection)

mms

[BibTex]

[BibTex]


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Rutherford Backscattering

Carstanjen, H. D.

In Nanoelectronics and Information Technology. Advanced Electronic Materials and Novel Devices, pages: 250-252, WILEY-VCH Verlag, Weinheim, Germany, 2012 (incollection)

mms

[BibTex]

[BibTex]

2005


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Support Vector Machines and Kernel Algorithms

Schölkopf, B., Smola, A.

In Encyclopedia of Biostatistics (2nd edition), Vol. 8, 8, pages: 5328-5335, (Editors: P Armitage and T Colton), John Wiley & Sons, NY USA, 2005 (inbook)

ei

[BibTex]

2005


[BibTex]


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Visual perception I: Basic principles

Wagemans, J., Wichmann, F., de Beeck, H.

In Handbook of Cognition, pages: 3-47, (Editors: Lamberts, K. , R. Goldstone), Sage, London, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Geckobot and waalbot: Small-scale wall climbing robots

Unver, O., Murphy, M., Sitti, M.

In Infotech@ Aerospace, pages: 6940, 2005 (incollection)

pi

[BibTex]

[BibTex]

2002


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Bayesian Inference of Visual Motion Boundaries

Fleet, D. J., Black, M. J., Nestares, O.

In Exploring Artificial Intelligence in the New Millennium, pages: 139-174, (Editors: Lakemeyer, G. and Nebel, B.), Morgan Kaufmann Pub., July 2002 (incollection)

Abstract
This chapter addresses an open problem in visual motion analysis, the estimation of image motion in the vicinity of occlusion boundaries. With a Bayesian formulation, local image motion is explained in terms of multiple, competing, nonlinear models, including models for smooth (translational) motion and for motion boundaries. The generative model for motion boundaries explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We formulate the posterior probability distribution over the models and model parameters, conditioned on the image sequence. Approximate inference is achieved with a combination of tools: A Bayesian filter provides for online computation; factored sampling allows us to represent multimodal non-Gaussian distributions and to propagate beliefs with nonlinear dynamics from one time to the next; and mixture models are used to simplify the computation of joint prediction distributions in the Bayesian filter. To efficiently represent such a high-dimensional space, we also initialize samples using the responses of a low-level motion-discontinuity detector. The basic formulation and computational model provide a general probabilistic framework for motion estimation with multiple, nonlinear models.

ps

pdf [BibTex]

2002


pdf [BibTex]


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Learning robot control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 983-987, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on learning control in robots.

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link (url) [BibTex]

link (url) [BibTex]


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Arm and hand movement control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 110-113, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on computational and biological research on arm and hand control.

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link (url) [BibTex]

link (url) [BibTex]


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Ion Channeling in Quasicrystals

Plachke, D., Carstanjen, H. D.

In Quasicrystals. An Introduction to Structure, Physical Properties and Applications, 55, pages: 280-304, Springer Series in Materials Science, Springer, Berlin [et al.], 2002 (incollection)

mms

[BibTex]

[BibTex]

1995


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Batting a ball: Dynamics of a rhythmic skill

Sternad, D., Schaal, S., Atkeson, C. G.

In Studies in Perception and Action, pages: 119-122, (Editors: Bardy, B.;Bostma, R.;Guiard, Y.), Erlbaum, Hillsdayle, NJ, 1995, clmc (inbook)

am

[BibTex]

1995


[BibTex]