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A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Zhu, J., Diehl, M., Schölkopf, B.

2nd Annual Conference on Learning for Dynamics and Control (L4DC), June 2020 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


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Mixed-curvature Variational Autoencoders

Skopek, O., Ganea, O., Becigneul, G.

8th International Conference on Learning Representations (ICLR), April 2020 (conference) Accepted

ei

link (url) [BibTex]

link (url) [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference) Accepted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

ei ps

arXiv [BibTex]

arXiv [BibTex]


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More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 (conference) To be published

ei

arXiv [BibTex]

arXiv [BibTex]


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Effect of the soft layer thickness of magnetization reversal process of exchange-spring nanomagnet patterns

Son, K., Schütz, G., Goering, E.

{Current Applied Physics}, 20(4):477-483, Elsevier B.V., Amsterdam, 2020 (article)

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DOI [BibTex]


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Tuning the magnetic properties of permalloy-based magnetoplasmonic crystals for sensor applications

Murzin, D. V., Belyaev, V. K., Groß, F., Gräfe, J., Rivas, M., Rodionova, V. V.

{Japanese Journal of Applied Physics}, 59(SE), IOP Publishing Ltd, Bristol, England, 2020 (article)

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DOI [BibTex]

DOI [BibTex]


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Creating zero-field skyrmions in exchange-biased multilayers through X-ray illumination

Guang, Y., Bykova, I., Liu, Y., Yu, G., Goering, E., Weigand, M., Gräfe, J., Kim, S. K., Zhang, J., Zhang, H., Yan, Z., Wan, C., Feng, J., Wang, X., Guo, C., Wei, H., Peng, Y., Tserkovnyak, Y., Han, X., Schütz, G.

{Nature Communications}, 11, Nature Publishing Group, London, 2020 (article)

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DOI [BibTex]

DOI [BibTex]


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Element-resolved study of the evolution of magnetic response in FexN compounds

Chen, Y., Gölden, D., Dirba, I., Huang, M., Gutfleisch, O., Nagel, P., Merz, M., Schuppler, S., Schütz, G., Alff, L., Goering, E.

{Journal of Magnetism and Magnetic Materials}, 498, NH, Elsevier, Amsterdam, 2020 (article)

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DOI [BibTex]

DOI [BibTex]


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The role of temperature and drive current in skyrmion dynamics

Litzius, K., Leliaert, J., Bassirian, P., Rodrigues, D., Kromin, S., Lemesh, I., Zazvorka, J., Lee, K., Mulkers, J., Kerber, N., Heinze, D., Keil, N., Reeve, R. M., Weigand, M., Van Waeyenberge, B., Schütz, G., Everschor-Sitte, K., Beach, G. S. D., Kläui, M.

{Nature Electronics}, 3(1):30-36, Springer Nature, London, 2020 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic flux penetration into micron-sized superconductor/ferromagnet bilayers

Simmendinger, J., Weigand, M., Schütz, G., Albrecht, J.

{Superconductor Science and Technology}, 33(2), IOP Pub., Bristol, 2020 (article)

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DOI [BibTex]

DOI [BibTex]


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Computationally Tractable Riemannian Manifolds for Graph Embeddings

Cruceru, C., Becigneul, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


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Interaction of hydrogen isotopes with flexible metal-organic frameworks

Bondorf, L.

Universität Stuttgart, Stuttgart, 2020 (mastersthesis)

mms

[BibTex]

[BibTex]


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Fabrication and temperature-dependent magnetic properties of large-area L10-FePt/Co exchange-spring magnet nanopatterns

Son, K., Schütz, G.

{Physica E: Low-Dimensional Systems And Nanostructures}, 115, North-Holland, Amsterdam, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Practical Accelerated Optimization on Riemannian Manifolds

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


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How to functionalise metal-organic frameworks to enable guest nanocluster embedment

King, J., Zhang, L., Doszczeczko, S., Sambalova, O., Luo, H., Rohman, F., Phillips, O., Borgschulte, A., Hirscher, M., Addicoat, M., Szilágyi, P. A.

{Journal of Materials Chemistry A}, 8(9):4889-4897, Royal Society of Chemistry, Cambridge, UK, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Constant Curvature Graph Convolutional Networks

Bachmann*, G., Becigneul*, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]


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Thermal nucleation and high-resolution imaging of submicrometer magnetic bubbles in thin thulium iron garnet films with perpendicular anisotropy

Büttner, F., Mawass, M. A., Bauer, J., Rosenberg, E., Caretta, L., Avci, C. O., Gräfe, J., Finizio, S., Vaz, C. A. F., Novakovic, N., Weigand, M., Litzius, K., Förster, J., Träger, N., Groß, F., Suzuki, D., Huang, M., Bartell, J., Kronast, F., Raabe, J., Schütz, G., Ross, C. A., Beach, G. S. D.

{Physical Review Materials}, 4(1), American Physical Society, College Park, MD, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

am ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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Generation and characterization of focused helical x-ray beams

Loetgering, L., Baluktsian, M., Keskinbora, K., Horstmeyer, R., Wilhein, T., Schütz, G., Eikema, K. S. E., Witte, S.

Science Advances, 6(7), American Association for the Advancement of Science, 2020 (article)

mms

Generation and characterization of focused helical x-ray beams link (url) DOI [BibTex]

Generation and characterization of focused helical x-ray beams link (url) DOI [BibTex]


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Materials for hydrogen-based energy storage - past, recent progress and future outlook

Hirscher, M., Yartys, V. A., Baricco, M., Bellosta von Colbe, J., Blanchard, D., Bowman Jr., R. C., Broom, D. P., Buckley, C. E., Chang, F., Chen, P., Cho, Y. W., Crivello, J., Cuevas, F., David, W. I. F., de Jongh, P. E., Denys, R. V., Dornheim, M., Felderhoff, M., Filinchuk, Y., Froudakis, G. E., Grant, D. M., Gray, E. M., Hauback, B. C., He, T., Humphries, T. D., Jensen, T. R., Kim, S., Kojima, Y., Latroche, M., Li, H., Lotostskyy, M. V., Makepeace, J. W., M\oller, K. T., Naheed, L., Ngene, P., Noréus, D., Nyg\aard, M. M., Orimo, S., Paskevicius, M., Pasquini, L., Ravnsbaek, D. B., Sofianos, M. V., Udovic, T. J., Vegge, T., Walker, G. S., Webb, C. J., Weidenthaler, C., Zlotea, C.

{Journal of Alloys and Compounds}, 827, Elsevier B.V., Lausanne, Switzerland, 2020 (article)

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DOI [BibTex]

DOI [BibTex]

2016


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Consistent Kernel Mean Estimation for Functions of Random Variables

Simon-Gabriel*, C. J., Ścibior*, A., Tolstikhin, I., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016, *joint first authors (conference)

ei

link (url) Project Page Project Page Project Page [BibTex]

2016


link (url) Project Page Project Page Project Page [BibTex]


A New Perspective and Extension of the Gaussian Filter
A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Garcia Cifuentes, C., Kappler, D., Schaal, S.

The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. The GF represents the belief of the current state by a Gaussian distribution, whose mean is an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF as the solution to a constrained optimization problem. From this new perspective, the GF is seen as a special case of a much broader class of filters, obtained by relaxing the constraint on the form of the approximate posterior. On this basis, we outline some conditions which potential generalizations have to satisfy in order to maintain the computational efficiency of the GF. We propose one concrete generalization which corresponds to the standard GF using a pseudo measurement instead of the actual measurement. Extending an existing GF implementation in this manner is trivial. Nevertheless, we show that this small change can have a major impact on the estimation accuracy.

am ics

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Understanding Probabilistic Sparse Gaussian Process Approximations

Bauer, M., van der Wilk, M., Rasmussen, C. E.

Advances in Neural Information Processing Systems 29, pages: 1533-1541, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B. K., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1930-1938, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Local-utopia Policy Selection for Multi-objective Reinforcement Learning

Parisi, S., Blank, A., Viernickel, T., Peters, J.

In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pages: 1-7, IEEE, December 2016 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


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Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 3612-3620, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, pages: 856-864, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Predictive and Self Triggering for Event-based State Estimation

Trimpe, S.

In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), pages: 3098-3105, Las Vegas, NV, USA, December 2016 (inproceedings)

am ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


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Catching heuristics are optimal control policies

Belousov, B., Neumann, G., Rothkopf, C., Peters, J.

Advances in Neural Information Processing Systems 29, pages: 1426-1434, (Editors: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett), Curran Associates, Inc., 30th Annual Conference on Neural Information Processing Systems, December 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Wireless actuation with functional acoustic surfaces
Wireless actuation with functional acoustic surfaces

Qiu, T., Palagi, S., Mark, A. G., Melde, K., Adams, F., Fischer, P.

Appl. Phys. Lett., 109(19):191602, November 2016, APL Editor's pick. APL News. (article)

Abstract
Miniaturization calls for micro-actuators that can be powered wirelessly and addressed individually. Here, we develop functional surfaces consisting of arrays of acoustically resonant microcavities, and we demonstrate their application as two-dimensional wireless actuators. When remotely powered by an acoustic field, the surfaces provide highly directional propulsive forces in fluids through acoustic streaming. A maximal force of similar to 0.45mN is measured on a 4 x 4 mm(2) functional surface. The response of the surfaces with bubbles of different sizes is characterized experimentally. This shows a marked peak around the micro-bubbles' resonance frequency, as estimated by both an analytical model and numerical simulations. The strong frequency dependence can be exploited to address different surfaces with different acoustic frequencies, thus achieving wireless actuation with multiple degrees of freedom. The use of the functional surfaces as wireless ready-to-attach actuators is demonstrated by implementing a wireless and bidirectional miniaturized rotary motor, which is 2.6 x 2.6 x 5 mm(3) in size and generates a stall torque of similar to 0.5 mN.mm. The adoption of micro-structured surfaces as wireless actuators opens new possibilities in the development of miniaturized devices and tools for fluidic environments that are accessible by low intensity ultrasound fields.

pf

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Incremental Imitation Learning of Context-Dependent Motor Skills

Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., Peters, J.

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 502-508, November 2016 (conference)

am ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Demonstration Based Trajectory Optimization for Generalizable Robot Motions

Koert, D., Maeda, G., Lioutikov, R., Neumann, G., Peters, J.

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 351-358, IEEE, November 2016 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis
Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages: 650-655, November 2016 (conference)

am ei

final link (url) DOI Project Page [BibTex]

final link (url) DOI Project Page [BibTex]


Nanomotors
Nanomotors

Alarcon-Correa, M., Walker (Schamel), D., Qiu, T., Fischer, P.

Eur. Phys. J.-Special Topics, 225(11-12):2241-2254, November 2016 (article)

Abstract
This minireview discusses whether catalytically active macromolecules and abiotic nanocolloids, that are smaller than motile bacteria, can self-propel. Kinematic reversibility at low Reynolds number demands that self-propelling colloids must break symmetry. Methods that permit the synthesis and fabrication of Janus nanocolloids are therefore briefly surveyed, as well as means that permit the analysis of the nanocolloids' motion. Finally, recent work is reviewed which shows that nanoagents are small enough to penetrate the complex inhomogeneous polymeric network of biological fluids and gels, which exhibit diverse rheological behaviors.

pf

DOI [BibTex]

DOI [BibTex]


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Deep Spiking Networks for Model-based Planning in Humanoids

Tanneberg, D., Paraschos, A., Peters, J., Rueckert, E.

IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pages: 656-661, IEEE, November 2016 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Anticipative Interaction Primitives for Human-Robot Collaboration

Maeda, G., Maloo, A., Ewerton, M., Lioutikov, R., Peters, J.

AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, pages: 325-330, November 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Role of Measurement Uncertainty in Optimal Control for Contact Interactions
Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

Abstract
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of pre- cise knowledge of the world, which is not an actual disturbance. We de- velop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.

am

arXiv [BibTex]

arXiv [BibTex]


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Unifying distillation and privileged information

Lopez-Paz, D., Schölkopf, B., Bottou, L., Vapnik, V.

International Conference on Learning Representations (ICLR), November 2016 (conference)

ei

Arxiv Project Page [BibTex]

Arxiv Project Page [BibTex]


Structured light enables biomimetic swimming and versatile locomotion of photoresponsive soft microrobots
Structured light enables biomimetic swimming and versatile locomotion of photoresponsive soft microrobots

Palagi, S., Mark, A. G., Reigh, S. Y., Melde, K., Qiu, T., Zeng, H., Parmeggiani, C., Martella, D., Sanchez-Castillo, A., Kapernaum, N., Giesselmann, F., Wiersma, D. S., Lauga, E., Fischer, P.

Nature Materials, 15(6):647–653, November 2016, Max Planck press release, Nature News & Views. (article)

Abstract
Microorganisms move in challenging environments by periodic changes in body shape. In contrast, current artificial microrobots cannot actively deform, exhibiting at best passive bending under external fields. Here, by taking advantage of the wireless, scalable and spatiotemporally selective capabilities that light allows, we show that soft microrobots consisting of photoactive liquid-crystal elastomers can be driven by structured monochromatic light to perform sophisticated biomimetic motions. We realize continuum yet selectively addressable artificial microswimmers that generate travelling-wave motions to self-propel without external forces or torques, as well as microrobots capable of versatile locomotion behaviours on demand. Both theoretical predictions and experimental results confirm that multiple gaits, mimicking either symplectic or antiplectic metachrony of ciliate protozoa, can be achieved with single microswimmers. The principle of using structured light can be extended to other applications that require microscale actuation with sophisticated spatiotemporal coordination for advanced microrobotic technologies.

pf

Video - Soft photo Micro-Swimmer DOI [BibTex]

Video - Soft photo Micro-Swimmer DOI [BibTex]


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Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

Xiao, L., Wang, J., Heidrich, W., Hirsch, M.

Computer Vision - ECCV 2016, Lecture Notes in Computer Science, LNCS 9907, Part III, pages: 734-749, (Editors: Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling), Springer, October 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Adaptive Training Strategies for BCIs

Sharma, D., Tanneberg, D., Grosse-Wentrup, M., Peters, J., Rueckert, E.

Cybathlon Symposium, October 2016 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Learning Where to Search Using Visual Attention
Learning Where to Search Using Visual Attention

Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

Abstract
One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.

am

Project Page [BibTex]

PDF Project Page [BibTex]


Parameter Learning for Improving Binary Descriptor Matching
Parameter Learning for Improving Binary Descriptor Matching

Sankaran, B., Ramalingam, S., Taguchi, Y.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016 (inproceedings)

Abstract
Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

am

[BibTex]

[BibTex]