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2017


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Robot Learning

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

In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

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Project Page [BibTex]

2017


Project Page [BibTex]


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Projected Newton-type methods in machine learning

Schmidt, M., Kim, D., Sra, S.

In Optimization for Machine Learning, pages: 305-330, MIT Press, Cambridge, MA, USA, 2011 (incollection)

Abstract
{We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.}

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link (url) [BibTex]

link (url) [BibTex]

2010


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Locally weighted regression for control

Ting, J., Vijayakumar, S., Schaal, S.

In Encyclopedia of Machine Learning, pages: 613-624, (Editors: Sammut, C.;Webb, G. I.), Springer, 2010, clmc (inbook)

Abstract
This is article addresses two topics: learning control and locally weighted regression.

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link (url) [BibTex]

2010


link (url) [BibTex]


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Physisorption in porous materials

Hirscher, M., Panella, B.

In Handbook of Hydrogen Storage, pages: 39-62, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2010 (incollection)

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[BibTex]

[BibTex]


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Adsorption technologies

Schmitz, B., Hirscher, M.

In Hydrogen and Fuel Cells, pages: 431-445, WILEY-VCH, Weinheim, 2010 (incollection)

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[BibTex]

[BibTex]

2007


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Dynamics systems vs. optimal control ? a unifying view

Schaal, S, Mohajerian, P., Ijspeert, A.

In Progress in Brain Research, (165):425-445, 2007, clmc (inbook)

Abstract
In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view.

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link (url) [BibTex]

2007


link (url) [BibTex]


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Micromagnetism-microstructure relations and the hysteresis loop

Goll, D.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 2: Micromagnetism, pages: 1023-1058, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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[BibTex]

[BibTex]


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Synchrotron radiation techniques based on X-ray magnetic circular dichroism

Schütz, G., Goering, E., Stoll, H.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 3: Materials Novel Techniques for Characterizing and Preparing Samples, pages: 1311-1363, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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[BibTex]

[BibTex]


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Micromagnetism-microstructure relations and the hysteresis loop

Goll, D.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 2: Micromagnetism, pages: 1023-1058, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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[BibTex]

[BibTex]


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Dissipative magnetization dynamics close to the adiabatic regime

Fähnle, M., Steiauf, D.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 1: Fundamental and Theory, pages: 282-302, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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[BibTex]

[BibTex]

2004


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Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A., Billard, A.

In The Neuroscience of Social Interaction, (1431):199-218, (Editors: Frith, C. D.;Wolpert, D.), Oxford University Press, Oxford, 2004, clmc (inbook)

Abstract
Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking - indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.

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link (url) [BibTex]

2004


link (url) [BibTex]


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Effect of Grain Boundary Phase Transitions on the Superplasticity in the Al-Zn System

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

In Nanomaterials by Severe Plastic Deformation, pages: 642-647, Wiley-VCH Verlag, Weinheim, 2004 (incollection)

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[BibTex]

[BibTex]