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2019


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EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Strecke, M., Stückler, J.

In International Conference on Computer Vision, October 2019, arXiv:1904.11781 (inproceedings)

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preprint Project page Poster [BibTex]

2019


preprint Project page Poster [BibTex]


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Accurate Vision-based Manipulation through Contact Reasoning

Kloss, A., Bauza, M., Wu, J., Tenenbaum, J. B., Rodriguez, A., Bohg, J.

In International Conference on Robotics and Automation, May 2019 (inproceedings) Submitted

Abstract
Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and in only partially observed environments, executing contact-based tasks often suffers from low accuracy. We present an approach that addresses these two challenges for the problem of vision-based manipulation. First, we propose to disentangle contact from motion optimization. Thereby, we improve planning efficiency by focusing computation on promising contact locations. Second, we use a hybrid approach for perception and state estimation that combines neural networks with a physically meaningful state representation. In simulation and real-world experiments on the task of planar pushing, we show that our method is more efficient and achieves a higher manipulation accuracy than previous vision-based approaches.

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Video link (url) [BibTex]

Video link (url) [BibTex]


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Learning Latent Space Dynamics for Tactile Servoing

Sutanto, G., Ratliff, N., Sundaralingam, B., Chebotar, Y., Su, Z., Handa, A., Fox, D.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2019, IEEE, International Conference on Robotics and Automation, May 2019 (inproceedings) Accepted

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pdf video [BibTex]

pdf video [BibTex]


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X-ray Optics Fabrication Using Unorthodox Approaches

Sanli, U., Baluktsian, M., Ceylan, H., Sitti, M., Weigand, M., Schütz, G., Keskinbora, K.

Bulletin of the American Physical Society, APS, 2019 (article)

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[BibTex]

[BibTex]


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Nanoscale detection of spin wave deflection angles in permalloy

Gross, F., Träger, N., Förster, J., Weigand, M., Schütz, G., Gräfe, J.

{Applied Physics Letters}, 114(1), American Institute of Physics, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Generation of switchable singular beams with dynamic metasurfaces

Yu, P., Li, J., Li, X., Schütz, G., Hirscher, M., Zhang, S., Liu, N.

{ACS Nano}, 13(6):7100-7106, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Extracting the dynamic magnetic contrast in time-resolved X-ray transmission microscopy

Schaffers, T., Feggeler, T., Pile, S., Meckenstock, R., Buchner, M., Spoddig, D., Ney, V., Farle, M., Wende, H., Wintz, S., Weigand, M., Ohldag, H., Ollefs, K, Ney, A.

{Nanomaterials}, 9(7), MDPI, Basel, Schweiz, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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gFORC: A graphics processing unit accelerated first-order reversal-curve calculator

Groß, F., Mart\’\inez-Garc\’\ia, J. C., Ilse, S. E., Schütz, G., Goering, E., Rivas, M., Gräfe, J.

{Journal of Applied Physics}, 126(16), AIP Publishing, New York, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Piezo-electrical control of gyration dynamics of magnetic vortices

Filianina, M., Baldrati, L., Hajiri, T., Litzius, K., Foerster, M., Aballe, L., Kläui, M.

{Applied Physics Letters}, 115(6), American Institute of Physics, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Coherent excitation of heterosymmetric spin waves with ultrashort wavelengths

Dieterle, G., Förster, J., Stoll, H., Semisalova, A. S., Finizio, S., Gangwar, A., Weigand, M., Noske, M., Fähnle, M., Bykova, I., Gräfe, J., Bozhko, D. A., Musiienko-Shmarova, H. Y., Tiberkevich, V., Slavin, A. N., Back, C. H., Raabe, J., Schütz, G., Wintz, S.

{Physical Review Letters}, 122(11), American Physical Society, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Reprogrammability and Scalability of Magnonic Fibonacci Quasicrystals

Lisiecki, F., Rychły, J., Kuświk, P., Głowiński, H., Kłos, J. W., Groß, F., Bykova, I., Weigand, M., Zelent, M., Goering, E. J., Schütz, G., Gubbiotti, G., Krawczyk, M., Stobiecki, F., Dubowik, J., Gräfe, J.

Physical Review Applied, 11, pages: 054003, 2019 (article)

Abstract
Magnonic crystals are systems that can be used to design and tune the dynamic properties of magnetization. Here, we focus on one-dimensional Fibonacci magnonic quasicrystals. We confirm the existence of collective spin waves propagating through the structure as well as dispersionless modes; the reprogammability of the resonance frequencies, dependent on the magnetization order; and dynamic spin-wave interactions. With the fundamental understanding of these properties, we lay a foundation for the scalable and advanced design of spin-wave band structures for spintronic, microwave, and magnonic applications.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Coordinated molecule-modulated magnetic phase with metamagnetism in metal-organic frameworks

Son, K., Kim, J. Y., Schütz, G., Kang, S. G., Moon, H. R., Oh, H.

{Inorganic Chemistry}, 58(14):8895-8899, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Scaling of intrinsic domain wall magnetoresistance with confinement in electromigrated nanocontacts

Reeve, R. M., Loescher, A., Kazemi, H., Dupé, B., Mawass, M., Winkler, T., Schönke, D., Miao, J., Litzius, K., Sedlmayr, N., Schneider, I., Sinova, J., Eggert, S., Kläui, M.

{Physical Review B}, 99(21), American Physical Society, Woodbury, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnons in a Quasicrystal: Propagation, Extinction, and Localization of Spin Waves in Fibonacci Structures

Lisiecki, F., Rychły, J., Kuświk, P., Głowiński, H., Kłos, J. W., Groß, F., Träger, N., Bykova, I., Weigand, M., Zelent, M., Goering, E. J., Schütz, G., Krawczyk, M., Stobiecki, F., Dubowik, J., Gräfe, J.

Physical Review Applied, 11, pages: 054061, 2019 (article)

Abstract
Magnonic quasicrystals exceed the possibilities of spin-wave (SW) manipulation offered by regular magnonic crystals, because of their more complex SW spectra with fractal characteristics. Here, we report the direct x-ray microscopic observation of propagating SWs in a magnonic quasicrystal, consisting of dipolar coupled permalloy nanowires arranged in a one-dimensional Fibonacci sequence. SWs from the first and second band as well as evanescent waves from the band gap between them are imaged. Moreover, additional mini band gaps in the spectrum are demonstrated, directly indicating an influence of the quasiperiodicity of the system. Finally, the localization of SW modes within the Fibonacci crystal is shown. The experimental results are interpreted using numerical calculations and we deduce a simple model to estimate the frequency position of the magnonic gaps in quasiperiodic structures. The demonstrated features of SW spectra in one-dimensional magnonic quasicrystals allow utilizing this class of metamaterials for magnonics and make them an ideal basis for future applications.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Structural and magnetic properties of FePt-Tb alloy thin films

Schmidt, N. Y., Laureti, S., Radu, F., Ryll, H., Luo, C., d\textquotesingleAcapito, F., Tripathi, S., Goering, E., Weller, D., Albrecht, M.

{Physical Review B}, 100(6), American Physical Society, Woodbury, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Tunable perpendicular exchange bias in oxide heterostructures

Kim, G., Khaydukov, Y., Bluschke, M., Suyolcu, Y. E., Christiani, G., Son, K., Dietl, C., Keller, T., Weschke, E., van Aken, P. A., Logvenov, G., Keimer, B.

{Physical Review Materials}, 3(8), American Physical Society, College Park, MD, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Automated Generation of Reactive Programs from Human Demonstration for Orchestration of Robot Behaviors

Berenz, V., Bjelic, A., Mainprice, J.

ArXiv, 2019 (article)

Abstract
Social robots or collaborative robots that have to interact with people in a reactive way are difficult to program. This difficulty stems from the different skills required by the programmer: to provide an engaging user experience the behavior must include a sense of aesthetics while robustly operating in a continuously changing environment. The Playful framework allows composing such dynamic behaviors using a basic set of action and perception primitives. Within this framework, a behavior is encoded as a list of declarative statements corresponding to high-level sensory-motor couplings. To facilitate non-expert users to program such behaviors, we propose a Learning from Demonstration (LfD) technique that maps motion capture of humans directly to a Playful script. The approach proceeds by identifying the sensory-motor couplings that are active at each step using the Viterbi path in a Hidden Markov Model (HMM). Given these activation patterns, binary classifiers called evaluations are trained to associate activations to sensory data. Modularity is increased by clustering the sensory-motor couplings, leading to a hierarchical tree structure. The novelty of the proposed approach is that the learned behavior is encoded not in terms of trajectories in a task space, but as couplings between sensory information and high-level motor actions. This provides advantages in terms of behavioral generalization and reactivity displayed by the robot.

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Support Video link (url) [BibTex]


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Interpreting first-order reversal curves beyond the Preisach model: An experimental permalloy microarray investigation

Groß, F., Ilse, S. E., Schütz, G., Gräfe, J., Goering, E.

{Physical Review B}, 99(6), American Physical Society, Woodbury, NY, 2019 (article)

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DOI [BibTex]


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Bistability of magnetic states in Fe-Pd nanocap arrays

Aravind, P. B., Heigl, M., Fix, M., Groß, F., Gräfe, J., Mary, A., Rajgowrav, C. R., Krupiński, M., Marszałek, M., Thomas, S., Anantharaman, M. R., Albrecht, M.

Nanotechnology, 30, pages: 405705, 2019 (article)

Abstract
Magnetic bistability between vortex and single domain states in nanostructures are of great interest from both fundamental and technological perspectives. In soft magnetic nanostructures, the transition from a uniform collinear magnetic state to a vortex state (or vice versa) induced by a magnetic field involves an energy barrier. If the thermal energy is large enough for overcoming this energy barrier, magnetic bistability with a hysteresis-free switching occurs between the two magnetic states. In this work, we tune this energy barrier by tailoring the composition of FePd alloys, which were deposited onto self-assembled particle arrays forming magnetic vortex structures on top of the particles. The bifurcation temperature, where a hysteresis-free transition occurs, was extracted from the temperature dependence of the annihilation and nucleation field which increases almost linearly with Fe content of the magnetic alloy. This study provides insights into the magnetization reversal process associated with magnetic bistability, which allows adjusting the bifurcation temperature range by the material properties of the nanosystem.

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link (url) [BibTex]

link (url) [BibTex]


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Learning to Disentangle Latent Physical Factors for Video Prediction

Zhu, D., Munderloh, M., Rosenhahn, B., Stückler, J.

In German Conference on Pattern Recognition (GCPR), 2019, to appear (inproceedings)

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dataset & evaluation code video preprint [BibTex]

dataset & evaluation code video preprint [BibTex]


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An international laboratory comparison study of volumetric and gravimetric hydrogen adsorption measurements

Hurst, K. E., Gennett, T., Adams, J., Allendorf, M. D., Balderas-Xicohténcatl, R., Bielewski, M., Edwards, B., Espinal, L., Fultz, B., Hirscher, M., Hudson, M. S. L., Hulvey, Z., Latroche, M., Liu, D., Kapelewski, M., Napolitano, E., Perry, Z. T., Purewal, J., Stavila, V., Veenstra, M., White, J. L., Yuan, Y., Zhou, H., Zlotea, C., Parilla, P.

{ChemPhysChem}, 20(15):1997-2009, Wiley-VCH, Weinheim, Germany, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Superior magnetic performance in FePt L10 nanomaterials

Son, K., Ryu, G. H., Jeong, H., Fink, L., Merz, M., Nagel, P., Schuppler, S., Richter, G., Goering, E., Schütz, G.

{Small}, 15(34), Wiley, Weinheim, Germany, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Vizualizing nanoscale spin waves using MAXYMUS

Gräfe, J., Weigand, M., Van Waeyenberge, B., Gangwar, A., Groß, F., Lisiecki, F., Rychly, J., Stoll, H., Träger, N., Förster, J., Stobiecki, F., Dubowik, J., Klos, H., Krwaczyk, M., Back, C. H., Goering, E. J., Schütz, G.

{Proceedings of SPIE}, 11090, SPIE, Bellingham, Washington, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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3D Birds-Eye-View Instance Segmentation

Elich, C., Engelmann, F., Kontogianni, T., Leibe, B.

In German Conference on Pattern Recognition (GCPR), 2019, arXiv:1904.02199, to appear (inproceedings)

ev

[BibTex]

[BibTex]


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Systematic experimental study on quantum sieving of hydrogen isotopes in metal-amide-imidazolate frameworks with narrow 1-D channels

Mondal, S. S., Kreuzer, A., Behrens, K., Schütz, G., Holdt, H., Hirscher, M.

{ChemPhysChem}, 20(10):1311-1315, Wiley-VCH, Weinheim, Germany, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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The route to supercurrent transparent ferromagnetic barriers in superconducting matrix

Ivanov, Y. P., Soltan, S., Albrecht, J., Goering, E., Schütz, G., Zhang, Z., Chuvilin, A.

{ACS Nano}, 13(5):5655-5661, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Artifacts from manganese reduction in rock samples prepared by focused ion beam (FIB) slicing for X-ray microspectroscopy

Macholdt, D. S., Förster, J., Müller, M., Weber, B., Kappl, M., Kilcoyne, A. L. D., Weigand, M., Leitner, J., Jochum, K. P., Pöhlker, C., Andreae, M. O.

{Geoscientific instrumentation, methods and data systems}, 8(1):97-111, Copernicus Publ., Göttingen, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic field dependence of mangetotransport properties of MgB2/CrO2 bilayer thin films

Alzayed, N. S., Shahabuddin, M., Ramey, S. M., Soltan, S.

{Journal of Superconductivity and Novel Magnetism}, 32(8):2447-2455, Springer Science + Business Media B.V., New York, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Mixed-state magnetotransport properties of MgB2 thin film prepared by pulsed laser deposition on an Al2O3 substrate

Alzayed, N. S., Shahabuddin, M., Ramey, S. M., Soltan, S.

{Journal of Materials Science: Materials in Electronics}, 30(2):1547-1552, Springer, Norwell, MA, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Comparison of theories of fast and ultrafast magnetization dynamics

Fähnle, M.

{Journal of Magnetism and Magnetic Materials}, 469, pages: 28-29, NH, Elsevier, Amsterdam, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Concepts for improving hydrogen storage in nanoporous materials

Broom, D. P., Webb, C. J., Fanourgakis, G. S., Froudakis, G. E., Trikalitis, P. N., Hirscher, M.

{International Journal of Hydrogen Energy}, 44(15):7768-7779, Elsevier, Amsterdam, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Controlling dislocation nucleation-mediatd plasticity in nanostructures via surface modification

Shin, J., Chen, L. Y., Sanli, U. T., Richter, G., Labat, S., Richard, M., Cornelius, T., Thomas, O., Gianola, D. S.

{Acta Materialia}, 166, pages: 572-586, Elsevier Science, Kidlington, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Reprogrammability and scalability of magnonic Fibonacci quasicrystals

Lisiecki, F., Rychly, J., Kuswik, P., Glowinski, H., Klos, J. W., Groß, F., Bykova, I., Weigand, M., Zelent, M., Goering, E. J., Schütz, G., Gubbiotti, G., Krawczyk, M., Stobiecki, F., Dubowik, J., Gräfe, J.

{Physical Review Applied}, 11(5), American Physical Society, College Park, Md. [u.a.], 2019 (article)

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DOI [BibTex]

DOI [BibTex]

2016


Thumb xl nonlinear approximate vs exact
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]

2016


PDF DOI 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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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]


Thumb xl screen shot 2019 01 07 at 11.54.16
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)

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final link (url) DOI Project Page [BibTex]

final link (url) DOI Project Page [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.

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arXiv [BibTex]

arXiv [BibTex]


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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.

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Project Page [BibTex]

PDF Project Page [BibTex]


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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.

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[BibTex]

[BibTex]


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A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pages: 3750-3756, October 2016 (conference)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Superpixel Convolutional Networks using Bilateral Inceptions

Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, 14th European Conference on Computer Vision, October 2016 (inproceedings)

Abstract
In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new “bilateral inception” module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception modules between the last CNN (1 × 1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.

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pdf supplementary poster Project Page Project Page [BibTex]

pdf supplementary poster Project Page Project Page [BibTex]


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Barrista - Caffe Well-Served

Lassner, C., Kappler, D., Kiefel, M., Gehler, P.

In ACM Multimedia Open Source Software Competition, ACM OSSC16, October 2016 (inproceedings)

Abstract
The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process. We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files. Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

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pdf link (url) DOI Project Page [BibTex]

pdf link (url) DOI Project Page [BibTex]


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Robust Gaussian Filtering using a Pseudo Measurement

Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.

In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Abstract
Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

am ics

Web link (url) DOI Project Page [BibTex]

Web link (url) DOI Project Page [BibTex]


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Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles

Widmaier, F., Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

am

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Optimizing for what matters: the Top Grasp Hypothesis

Kappler, D., Schaal, S., Bohg, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
In this paper, we consider the problem of robotic grasping of objects when only partial and noisy sensor data of the environment is available. We are specifically interested in the problem of reliably selecting the best hypothesis from a whole set. This is commonly the case when trying to grasp an object for which we can only observe a partial point cloud from one viewpoint through noisy sensors. There will be many possible ways to successfully grasp this object, and even more which will fail. We propose a supervised learning method that is trained with a ranking loss. This explicitly encourages that the top-ranked training grasp in a hypothesis set is also positively labeled. We show how we adapt the standard ranking loss to work with data that has binary labels and explain the benefits of this formulation. Additionally, we show how we can efficiently optimize this loss with stochastic gradient descent. In quantitative experiments, we show that we can outperform previous models by a large margin.

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pdf DOI Project Page [BibTex]


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Exemplar-based Prediction of Object Properties from Local Shape Similarity

Bohg, J., Kappler, D., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
We propose a novel method that enables a robot to identify a graspable object part of an unknown object given only noisy and partial information that is obtained from an RGB-D camera. Our method combines the benefits of local with the advantages of global methods. It learns a classifier that takes a local shape representation as input and outputs the probability that a grasp applied at this location will be successful. Given a query data point that is classified in this way, we can retrieve all the locally similar training data points and use them to predict latent global object shape. This information may help to further prune positively labeled grasp hypotheses based on, e.g. relation to the predicted average global shape or suitability for a specific task. This prediction can also guide scene exploration to prune object shape hypotheses. To learn the function that maps local shape to grasp stability we use a Random Forest Classifier. We show that our method reaches the same classification performance as the current state-of-the-art on this dataset which uses a Convolutional Neural Network. Additionally, we exploit the natural ability of the Random Forest to cluster similar data. For a positively predicted query data point, we retrieve all the locally similar training data points that are associated with the same leaf nodes of the Random Forest. The main insight from this work is that local object shape that affords a grasp is also a good predictor of global object shape. We empirically support this claim with quantitative experiments. Additionally, we demonstrate the predictive capability of the method on some real data examples.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]