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2012


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Elastomer surfaces with directionally dependent adhesion strength and their use in transfer printing with continuous roll-to-roll applications

Yang, S. Y., Carlson, A., Cheng, H., Yu, Q., Ahmed, N., Wu, J., Kim, S., Sitti, M., Ferreira, P. M., Huang, Y., others,

Advanced Materials, 24(16):2117-2122, WILEY-VCH Verlag, 2012 (article)

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[BibTex]

2012


[BibTex]


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Effect of retraction speed on adhesion of elastomer fibrillar structures

Abusomwan, U., Sitti, M.

Applied Physics Letters, 101(21):211907, AIP, 2012 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Model-free reinforcement learning of impedance control in stochastic environments

Stulp, Freek, Buchli, Jonas, Ellmer, Alice, Mistry, Michael, Theodorou, Evangelos A., Schaal, S.

Autonomous Mental Development, IEEE Transactions on, 4(4):330-341, 2012 (article)

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[BibTex]

[BibTex]


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Die Stabilität des stromtragenden Zustands in MgB2 Schichten mit modifizierter Mikrostruktur

Treiber, S.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]


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Accelerated diffusion and phase transformations in Co-Cu alloys driven by the severe plastic deformation

Straumal, B. B., Mazilkin, A. A., Baretzky, B., Schütz, G., Rabkin, E., Valiev, R. Z.

{Special Issue on Advanced Materials Science in Bulk Nanostructured Metals}, 53(1):63-71, 2012 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unusual flux jumps above 12 K in non-homogeneous MgB2 thin films

Treiber, S., Stahl, C., Schütz, G., Albrecht, J.

{Superconductor Science \& Technology}, 25, 2012 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Ferromagnetism of nanostructured zinc oxide films

Straumal, B. B., Mazilkin, A. A., Protasova, S. G., Straumal, P. B., Myatiev, A. A., Schütz, G., Goering, E., Baretzky, B.

{The Physics of Metals and Metallography}, 113(13):1244-1256, 2012 (article)

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DOI [BibTex]

DOI [BibTex]


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Frequencies and polarization vectors of phonons: Results from force constants which are fitted to experimental data or calculated ab initio

Illg, C., Meyer, B., Fähnle, M.

{Physical Review B}, 86(17), Published by the American Physical Society through the American Institute of Physics, Woodbury, NY, 2012 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Grain boundary wetting by a second solid phase in the Zr-Nb alloys

Straumal, B. B., Gornakova, A. S., Kucheev, Y. O., Baretzky, B., Nekrasov, A. N.

{Journal of Materials Engineering and Performance}, 21(5):721-724, 2012 (article)

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DOI [BibTex]

DOI [BibTex]


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Grain boundary wetting in the NdFeB-based hard magnetic alloys

Straumal, B. B., Kucheev, Y. O., Yatskovskaya, I. L., Mogilnikova, I. V., Schütz, G., Nekrasov, A. N., Baretzky, B.

{Journal of Materials Science}, 47(24):8352-8359, 2012 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Probabilistic depth image registration incorporating nonvisual information

Wüthrich, M., Pastor, P., Righetti, L., Billard, A., Schaal, S.

In 2012 IEEE International Conference on Robotics and Automation, pages: 3637-3644, IEEE, Saint Paul, USA, 2012 (inproceedings)

Abstract
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Consumer Depth Cameras for Computer Vision - Research Topics and Applications

Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K.

Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

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workshop publisher's site [BibTex]

workshop publisher's site [BibTex]


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Spatial Measures between Human Poses for Classification and Understanding

Soren Hauberg, Kim S. Pedersen

In Articulated Motion and Deformable Objects, 7378, pages: 26-36, LNCS, (Editors: Perales, Francisco J. and Fisher, Robert B. and Moeslund, Thomas B.), Springer Berlin Heidelberg, 2012 (inproceedings)

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Publishers site Project Page [BibTex]

Publishers site Project Page [BibTex]


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A Geometric Take on Metric Learning

Hauberg, S., Freifeld, O., Black, M. J.

In Advances in Neural Information Processing Systems (NIPS) 25, pages: 2033-2041, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract
Multi-metric learning techniques learn local metric tensors in different parts of a feature space. With such an approach, even simple classifiers can be competitive with the state-of-the-art because the distance measure locally adapts to the structure of the data. The learned distance measure is, however, non-metric, which has prevented multi-metric learning from generalizing to tasks such as dimensionality reduction and regression in a principled way. We prove that, with appropriate changes, multi-metric learning corresponds to learning the structure of a Riemannian manifold. We then show that this structure gives us a principled way to perform dimensionality reduction and regression according to the learned metrics. Algorithmically, we provide the first practical algorithm for computing geodesics according to the learned metrics, as well as algorithms for computing exponential and logarithmic maps on the Riemannian manifold. Together, these tools let many Euclidean algorithms take advantage of multi-metric learning. We illustrate the approach on regression and dimensionality reduction tasks that involve predicting measurements of the human body from shape data.

ps

PDF Youtube Suppl. material Poster Project Page [BibTex]

PDF Youtube Suppl. material Poster Project Page [BibTex]


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Impact and Surface Tension in Water: a Study of Landing Bodies

Shih, B., Laham, L., Lee, K. J., Krasnoff, N., Diller, E., Sitti, M.

Bio-inspired Robotics Final Project, Carnegie Mellon University, 2012 (article)

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[BibTex]

[BibTex]


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Design and rolling locomotion of a magnetically actuated soft capsule endoscope

Yim, S., Sitti, M.

IEEE Transactions on Robotics, 28(1):183-194, IEEE, 2012 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Design and manufacturing of a controllable miniature flapping wing robotic platform

Arabagi, V., Hines, L., Sitti, M.

The International Journal of Robotics Research, 31(6):785-800, SAGE Publications Sage UK: London, England, 2012 (article)

pi

[BibTex]

[BibTex]


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Chemotactic steering of bacteria propelled microbeads

Kim, D., Liu, A., Diller, E., Sitti, M.

Biomedical microdevices, 14(6):1009-1017, Springer US, 2012 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation

Stulp, F., Theodorou, E., Schaal, S.

IEEE Transactions on Robotics, 2012 (article)

am

[BibTex]

[BibTex]


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Hartmagnetische L10-FePt basierte gro\ssflächige Nanomuster mittels Nanoimprint-Lithografie

Bublat, T.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]


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Magnetic proximity effect in YBa2Cu3O7 / La2/3Ca1/3MnO3 and YBa2Cu3O7 / LaMnO3+δsuperlattices

Satapathy, D. K., Uribe-Laverde, M. A., Marozau, I., Malik, V. K., Das, S., Wagner, T., Marcelot, C., Stahn, J., Brück, S., Rühm, A., Macke, S., Tietze, T., Goering, E., Frañó, A., Kim, J., Wu, M., Benckiser, E., Keimer, B., Devishvili, A., Toperverg, B. P., Merz, M., Nagel, P., Schuppler, S., Bernhard, C.

{Physical Review Letters}, 108, 2012 (article)

mms

DOI [BibTex]

DOI [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)

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[BibTex]

[BibTex]


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Noble gases and microporous frameworks; from interaction to application

Soleimani Dorcheh, A., Denysenko, D., Volkmer, D., Donner, W., Hirscher, M.

{Microporous and Mesoporous Materials}, 162, pages: 64-68, Elsevier, Amsterdam, 2012 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Note: Unique characterization possibilities in the ultra high vacuum scanning transmission x-ray microscope (UHV-STXM) "MAXYMUS" using a rotatable permanent magnetic field up to 0.22 T

Nolle, D., Weigand, M., Audehm, P., Goering, E., Wiesemann, U., Wolter, C., Nolle, E., Schütz, G.

{Review of Scientific Instruments}, 83(4), 2012 (article)

mms

DOI [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]


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Microstructure and superconducting properties of MgB2 films prepared by solid state reaction of multilayer precursors of the elements

Kugler, B., Stahl, C., Treiber, S., Soltan, S., Haug, S., Schütz, G., Albrecht, J.

{Thin Solid Films}, 520, pages: 6985-6988, 2012 (article)

mms

DOI [BibTex]

DOI [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.

am

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. 

am

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.

am

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

am

link (url) [BibTex]

link (url) [BibTex]