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2018


Thumb xl encyclop med robotics
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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Impact of Trunk Orientation for Dynamic Bipedal Locomotion

Drama, O.

Dynamic Walking Conference, May 2018 (talk)

Abstract
Impact of trunk orientation for dynamic bipedal locomotion My research revolves around investigating the functional demands of bipedal running, with focus on stabilizing trunk orientation. When we think about postural stability, there are two critical questions we need to answer: What are the necessary and sufficient conditions to achieve and maintain trunk stability? I am concentrating on how morphology affects control strategies in achieving trunk stability. In particular, I denote the trunk pitch as the predominant morphology parameter and explore the requirements it imposes on a chosen control strategy. To analyze this, I use a spring loaded inverted pendulum model extended with a rigid trunk, which is actuated by a hip motor. The challenge for the controller design here is to have a single hip actuator to achieve two coupled tasks of moving the legs to generate motion and stabilizing the trunk. I enforce orthograde and pronograde postures and aim to identify the effect of these trunk orientations on the hip torque and ground reaction profiles for different control strategies.

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Impact of trunk orientation for dynamic bipedal locomotion [DW 2018] link (url) Project Page [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]

2015


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Untethered Magnetic Micromanipulation

Diller, E., Sitti, M.

In Micro-and Nanomanipulation Tools, 13, 10, Wiley-VCH Verlag GmbH & Co. KGaA, November 2015 (inbook)

Abstract
This chapter discusses the methods and state of the art in microscale manipulation in remote environments using untethered microrobotic devices. It focuses on manipulation at the size scale of tens to hundreds of microns, where small size leads to a dominance of microscale physical effects and challenges in fabrication and actuation. To motivate the challenges of operating at this size scale, the chapter includes coverage of the physical forces relevant to microrobot motion and manipulation below the millimeter-size scale. It then introduces the actuation methods commonly used in untethered manipulation schemes, with particular focus on magnetic actuation due to its wide use in the field. The chapter divides these manipulation techniques into two types: contact manipulation, which relies on direct pushing or grasping of objects for motion, and noncontact manipulation, which relies indirectly on induced fluid flow from the microrobot motion to move objects without any direct contact.

pi

DOI Project Page [BibTex]

2015


DOI Project Page [BibTex]


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

ei

[BibTex]

[BibTex]


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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Lernende Roboter

Trimpe, S.

In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) (inbook)

am ics

link (url) [BibTex]

link (url) [BibTex]


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Autonomous Robots

Schaal, S.

In Jahrbuch der Max-Planck-Gesellschaft, May 2015 (incollection)

am

[BibTex]

[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

ei

[BibTex]

[BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei

[BibTex]

[BibTex]


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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

ei

[BibTex]

[BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei

[BibTex]

[BibTex]


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Perception of Deformable Objects and Compliant Manipulation for Service Robots

Stueckler, J., Behnke, S.

In Soft Robotics: From Theory to Applications, Springer, 2015 (inbook)

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link (url) [BibTex]

link (url) [BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


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Tacit Learning for Emergence of Task-Related Behaviour through Signal Accumulation

Berenz, V., Alnajjar, F., Hayashibe, M., Shimoda, S.

In Emergent Trends in Robotics and Intelligent Systems: Where is the Role of Intelligent Technologies in the Next Generation of Robots?, pages: 31-38, Springer International Publishing, Cham, 2015 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Annales des Concours 2015 PC Mathématiques, Informatique

Batog, G., Dumont, J., Puyhaubert, V.

In corrigés des problèmes posés aux concours CCP, Centrale/Supélec, Mines/Ponts, X/ENS, 2015 (inbook)

H&K Éditions [BibTex]

H&K Éditions [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]


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Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., Schaal, S.

In Springer Handbook of Robotics 2nd Edition, pages: 1371-1394, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015 (incollection)

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[BibTex]

[BibTex]

2003


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Learning Control and Planning from the View of Control Theory and Imitation

Peters, J., Schaal, S.

NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

Abstract
Learning control and planning in high dimensional continuous state-action systems, e.g., as needed in a humanoid robot, has so far been a domain beyond the applicability of generic planning techniques like reinforcement learning and dynamic programming. This talk describes an approach we have taken in order to enable complex robotics systems to learn to accomplish control tasks. Adaptive learning controllers equipped with statistical learning techniques can be used to learn tracking controllers -- missing state information and uncertainty in the state estimates are usually addressed by observers or direct adaptive control methods. Imitation learning is used as an ingredient to seed initial control policies whose output is a desired trajectory suitable to accomplish the task at hand. Reinforcement learning with stochastic policy gradients using a natural gradient forms the third component that allows refining the initial control policy until the task is accomplished. In comparison to general learning control, this approach is highly prestructured and thus more domain specific. However, it seems to be a theoretically clean and feasible strategy for control systems of the complexity that we need to address.

ei

Web [BibTex]

2003


Web [BibTex]


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Recurrent neural networks from learning attractor dynamics

Schaal, S., Peters, J.

NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Abstract
Many forms of recurrent neural networks can be understood in terms of dynamic systems theory of difference equations or differential equations. Learning in such systems corresponds to adjusting some internal parameters to obtain a desired time evolution of the network, which can usually be characterized in term of point attractor dynamics, limit cycle dynamics, or, in some more rare cases, as strange attractor or chaotic dynamics. Finding a stable learning process to adjust the open parameters of the network towards shaping the desired attractor type and basin of attraction has remain a complex task, as the parameter trajectories during learning can lead the system through a variety of undesirable unstable behaviors, such that learning may never succeed. In this presentation, we review a recently developed learning framework for a class of recurrent neural networks that employs a more structured network approach. We assume that the canonical system behavior is known a priori, e.g., it is a point attractor or a limit cycle. With either supervised learning or reinforcement learning, it is possible to acquire the transformation from a simple representative of this canonical behavior (e.g., a 2nd order linear point attractor, or a simple limit cycle oscillator) to the desired highly complex attractor form. For supervised learning, one shot learning based on locally weighted regression techniques is possible. For reinforcement learning, stochastic policy gradient techniques can be employed. In any case, the recurrent network learned by these methods inherits the stability properties of the simple dynamic system that underlies the nonlinear transformation, such that stability of the learning approach is not a problem. We demonstrate the success of this approach for learning various skills on a humanoid robot, including tasks that require to incorporate additional sensory signals as coupling terms to modify the recurrent network evolution on-line.

ei

Web [BibTex]

Web [BibTex]


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Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Rademacher and Gaussian averages in Learning Theory

Bousquet, O.

Universite de Marne-la-Vallee, March 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Introduction: Robots with Cognition?

Franz, MO.

6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Abstract
Using robots as models of cognitive behaviour has a long tradition in robotics. Parallel to the historical development in cognitive science, one observes two major, subsequent waves in cognitive robotics. The first is based on ideas of classical, cognitivist Artificial Intelligence (AI). According to the AI view of cognition as rule-based symbol manipulation, these robots typically try to extract symbolic descriptions of the environment from their sensors that are used to update a common, global world representation from which, in turn, the next action of the robot is derived. The AI approach has been successful in strongly restricted and controlled environments requiring well-defined tasks, e.g. in industrial assembly lines. AI-based robots mostly failed, however, in the unpredictable and unstructured environments that have to be faced by mobile robots. This has provoked the second wave in cognitive robotics which tries to achieve cognitive behaviour as an emergent property from the interaction of simple, low-level modules. Robots of the second wave are called animats as their architecture is designed to closely model aspects of real animals. Using only simple reactive mechanisms and Hebbian-type or evolutionary learning, the resulting animats often outperformed the highly complex AI-based robots in tasks such as obstacle avoidance, corridor following etc. While successful in generating robust, insect-like behaviour, typical animats are limited to stereotyped, fixed stimulus-response associations. If one adopts the view that cognition requires a flexible, goal-dependent choice of behaviours and planning capabilities (H.A. Mallot, Kognitionswissenschaft, 1999, 40-48) then it appears that cognitive behaviour cannot emerge from a collection of purely reactive modules. It rather requires environmentally decoupled structures that work without directly engaging the actions that it is concerned with. This poses the current challenge to cognitive robotics: How can we build cognitive robots that show the robustness and the learning capabilities of animats without falling back into the representational paradigm of AI? The speakers of the symposium present their approaches to this question in the context of robot navigation and sensorimotor learning. In the first talk, Prof. Helge Ritter introduces a robot system for imitation learning capable of exploring various alternatives in simulation before actually performing a task. The second speaker, Angelo Arleo, develops a model of spatial memory in rat navigation based on his electrophysiological experiments. He validates the model on a mobile robot which, in some navigation tasks, shows a performance comparable to that of the real rat. A similar model of spatial memory is used to investigate the mechanisms of territory formation in a series of robot experiments presented by Prof. Hanspeter Mallot. In the last talk, we return to the domain of sensorimotor learning where Ralf M{\"o}ller introduces his approach to generate anticipatory behaviour by learning forward models of sensorimotor relationships.

ei

Web [BibTex]

Web [BibTex]


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Support Vector Machines

Schölkopf, B., Smola, A.

In Handbook of Brain Theory and Neural Networks (2nd edition), pages: 1119-1125, (Editors: MA Arbib), MIT Press, Cambridge, MA, USA, 2003 (inbook)

ei

[BibTex]

[BibTex]


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Extension of the nu-SVM range for classification

Perez-Cruz, F., Weston, J., Herrmann, D., Schölkopf, B.

In Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, 190, pages: 179-196, NATO Science Series III: Computer and Systems Sciences, (Editors: J Suykens and G Horvath and S Basu and C Micchelli and J Vandewalle), IOS Press, Amsterdam, 2003 (inbook)

ei

[BibTex]

[BibTex]


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An Introduction to Support Vector Machines

Schölkopf, B.

In Recent Advances and Trends in Nonparametric Statistics , pages: 3-17, (Editors: MG Akritas and DN Politis), Elsevier, Amsterdam, The Netherlands, 2003 (inbook)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Statistical Learning and Kernel Methods in Bioinformatics

Schölkopf, B., Guyon, I., Weston, J.

In Artificial Intelligence and Heuristic Methods in Bioinformatics, 183, pages: 1-21, 3, (Editors: P Frasconi und R Shamir), IOS Press, Amsterdam, The Netherlands, 2003 (inbook)

ei

[BibTex]

[BibTex]


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Statistical Learning and Kernel Methods

Navia-Vázquez, A., Schölkopf, B.

In Adaptivity and Learning—An Interdisciplinary Debate, pages: 161-186, (Editors: R.Kühn and R Menzel and W Menzel and U Ratsch and MM Richter and I-O Stamatescu), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

ei

[BibTex]

[BibTex]


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A Short Introduction to Learning with Kernels

Schölkopf, B., Smola, A.

In Proceedings of the Machine Learning Summer School, Lecture Notes in Artificial Intelligence, Vol. 2600, pages: 41-64, LNAI 2600, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Heidelberg, Germany, 2003 (inbook)

ei

[BibTex]

[BibTex]


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Bayesian Kernel Methods

Smola, A., Schölkopf, B.

In Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Lecture Notes in Computer Science, Vol. 2600, LNAI 2600, pages: 65-117, 0, (Editors: S Mendelson and AJ Smola), Springer, Berlin, Germany, 2003 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


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Stability of ensembles of kernel machines

Elisseeff, A., Pontil, M.

In 190, pages: 111-124, NATO Science Series III: Computer and Systems Science, (Editors: Suykens, J., G. Horvath, S. Basu, C. Micchelli and J. Vandewalle), IOS press, Netherlands, 2003 (inbook)

ei

[BibTex]

[BibTex]


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Investigation of the Initial Oxidation of Surfaces of Quasicrystals by High-Resolution RBS and ERDA

Plachke, D., Khellaf, A., Kurth, M., Szökefalvi-Nagy, A., Carstanjen, H. D.

In Quasicrystals: Structure and Physical Properties, pages: 598-614, Wiley-VCH GmbH & Co. KGaA, Weinheim, 2003 (incollection)

mms

[BibTex]

[BibTex]


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AMOC in positron and positronium chemistry

Stoll, H., Castellaz, P., Siegle, A.

In Principles and Applications of Positron and Positronium Chemistry, pages: 344-366, World Scientific Publishers, Singapore, 2003 (incollection)

mms

[BibTex]

[BibTex]