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2003


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The magnetic transmission X-ray microscopy project at BESSY II

Eimüller, T., Niemann, B., Guttmann, P., Fischer, P., Englisch, U., Vatter, R., Wolter, C., Seiffert, S., Schmahl, G., Schütz, G.

{Journal de Physique IV}, 104, pages: 91-94, 2003 (article)

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2003


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Ab-initio statistical mechanics for ordered compounds: single-defect theory vs. cluster-expansion techniques

Drautz, R., Schultz, I., Lechermann, F., Fähnle, M.

{Physica Status Solidi B}, 240(1):37-44, 2003 (article)

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[BibTex]

[BibTex]


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Magnetic imaging with soft x-ray microscopies

Fischer, P., Denbeaux, G., Stoll, H., Puzic, A., Raabe, J., Nolting, F., Eimüller, T., Schütz, G.

{Journal de Physique IV}, 104, pages: 471-476, 2003 (article)

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Hydrogen storage in carbon nanotubes

Hirscher, M., Becher, M.

{Journal of Nanoscience and Nanotechnology}, 3(1/2):3-17, 2003 (article)

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[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)

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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)

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[BibTex]


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Strong influence on the electronic structure of Pt adatoms and clusters on graphite

Fauth, K., He\ssler, M., Batchelor, D., Schütz, G.

{Surface Science}, 529(3):397-402, 2003 (article)

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[BibTex]


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NMR studies of hydrogen diffusion in the dihydrides of hafnium

Gottwald, J., Majer, G., Peterson, D. T., Barnes, R. G.

{Journal of Alloys and Compounds}, 356-357, pages: 274-278, 2003 (article)

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The +/-45 degrees correlation interferometer as a means to measure phase noise of parametric origin

Rubiola, E., Giordano, V., Stoll, H.

{IEEE Transactions on Instrumentation and Measurement}, 52, pages: 182-188, 2003 (article)

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[BibTex]

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Piezoelectrically actuated four-bar mechanism with two flexible links for micromechanical flying insect thorax

Sitti, M.

IEEE/ASME transactions on mechatronics, 8(1):26-36, IEEE, 2003 (article)

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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)

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Project Page [BibTex]

Project Page [BibTex]


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Slow removal of vacancies in B2-Ni52Al48 upon long-term low-temperature annealing

Zhang, X. Y., Sprengel, W., Reichle, K. J., Blaurock, K., Henes, R., Schaefer, H. E.

{Physical Review B}, 68(22), 2003 (article)

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Magnetic domain structure in SmCo 2 : 17 permanent magnets

Zhang, Y., Tang, W., Hadjipanayis, G. C., Chen, C. H., Goll, D., Kronmüller, H.

{IEEE Transactions on Magnetics}, 39(5):2905-2907, 2003 (article)

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Energy loss and charge state dependency of swift Nq+ ions scattered off a Pt(110)(1 x 2) surface

Robin, A., Hatke, N., Jensen, J., Plachke, D., Carstanjen, H. D., Heiland, W.

{Nuclear Instruments \& Methods in Physics Research B-Beam Interactions with Materials and Atoms}, 209, pages: 259-264, 2003 (article)

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Preparation and properties of [NdFeBx/Nbz]n multi-layer films

Tsai, J. L., Chin, T. S., Yao, Y. D., Melsheimer, A., Fischer, S. F., Dragon, T., Kelsch, M., Kronmüller, H.

{Physica B-Condensed Matter}, 327(2-4):283-286, 2003 (article)

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[BibTex]

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Multilayered magnetic nanostrips studied by transmission X-ray microscopy

Eimüller, T., Fischer, P., Guttmann, P., Denbeaux, G., Scholz, M., Köhler, M., Schmahl, G., Bayreuther, G., Schütz, G.

{Journal de Physique IV}, 104, pages: 483-486, 2003 (article)

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[BibTex]


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Imaging magnetic domain structures with soft X-ray microscopy

Fischer, P., Eimüller, T., Schütz, G., Denbeaux, G.

{Structural Chemistry}, 14(1):39-47, 2003 (article)

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Hydrogen solubility and diffusivity in amorphous La14Ni86 films

Cuevas, F., Hirscher, M.

{Acta Materialia}, 51, pages: 701-712, 2003 (article)

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[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)

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Hydrogen interaction with carbon nanostructures - current situation and future prospects

Orimo, S., Züttel, A., Schlapbach, L., Majer, G., Fukunaga, T., Fujii, H.

{Journal of Alloys and Compounds}, 356-357, pages: 716-719, 2003 (article)

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Desorption of hydrogen from blowing agents used for foaming metals

von Zeppelin, F., Hirscher, M., Stanzick, H., Banhart, J.

{Composites Science and Technology}, 63, pages: 2293-2300, 2003 (article)

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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)

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Entropic wetting of a colloidal rod-sphere mixture

Roth, R., Brader, J. M., Schmidt, M.

{Europhysics Letters}, 63(4):549-555, 2003 (article)

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[BibTex]

[BibTex]

1997


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Locally weighted learning

Atkeson, C. G., Moore, A. W., Schaal, S.

Artificial Intelligence Review, 11(1-5):11-73, 1997, clmc (article)

Abstract
This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, distance functions, smoothing parameters, weighting functions, global tuning, local tuning, interference.

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link (url) [BibTex]

1997


link (url) [BibTex]


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Locally weighted learning for control

Atkeson, C. G., Moore, A. W., Schaal, S.

Artificial Intelligence Review, 11(1-5):75-113, 1997, clmc (article)

Abstract
Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, forward models, inverse models, linear quadratic regulation (LQR), shifting setpoint algorithm, dynamic programming.

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link (url) [BibTex]

link (url) [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. 

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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.

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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

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link (url) [BibTex]

link (url) [BibTex]

1995


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A kendama learning robot based on a dynamic optimization theory

Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Osu, R., Nakano, E., Kawato, M.

In Preceedings of the 4th IEEE International Workshop on Robot and Human Communication (RO-MAN’95), pages: 327-332, Tokyo, July 1995, clmc (inproceedings)

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[BibTex]

1995


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Visual tracking for moving multiple objects: an integration of vision and control

Sitti, M, Bozma, I, Denker, A

In Industrial Electronics, 1995. ISIE’95., Proceedings of the IEEE International Symposium on, 2, pages: 535-540, 1995 (inproceedings)

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[BibTex]

[BibTex]


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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)

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[BibTex]

[BibTex]


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Memory-based neural networks for robot learning

Atkeson, C. G., Schaal, S.

Neurocomputing, 9, pages: 1-27, 1995, clmc (article)

Abstract
This paper explores a memory-based approach to robot learning, using memory-based neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memory-based, robot learning, locally weighted regression, nearest neighbor, local models.

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link (url) [BibTex]

link (url) [BibTex]