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2017


On the Design of {LQR} Kernels for Efficient Controller Learning
On the Design of LQR Kernels for Efficient Controller Learning

Marco, A., Hennig, P., Schaal, S., Trimpe, S.

Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), pages: 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (conference)

Abstract
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

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arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]

2017


arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI Project Page [BibTex]


Optimizing Long-term Predictions for Model-based Policy Search
Optimizing Long-term Predictions for Model-based Policy Search

Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., Trimpe, S.

Proceedings of 1st Annual Conference on Robot Learning (CoRL), 78, pages: 227-238, (Editors: Sergey Levine and Vincent Vanhoucke and Ken Goldberg), 1st Annual Conference on Robot Learning, November 2017 (conference)

Abstract
We propose a novel long-term optimization criterion to improve the robustness of model-based reinforcement learning in real-world scenarios. Learning a dynamics model to derive a solution promises much greater data-efficiency and reusability compared to model-free alternatives. In practice, however, modelbased RL suffers from various imperfections such as noisy input and output data, delays and unmeasured (latent) states. To achieve higher resilience against such effects, we propose to optimize a generative long-term prediction model directly with respect to the likelihood of observed trajectories as opposed to the common approach of optimizing a dynamics model for one-step-ahead predictions. We evaluate the proposed method on several artificial and real-world benchmark problems and compare it to PILCO, a model-based RL framework, in experiments on a manipulation robot. The results show that the proposed method is competitive compared to state-of-the-art model learning methods. In contrast to these more involved models, our model can directly be employed for policy search and outperforms a baseline method in the robot experiment.

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PDF Project Page [BibTex]

PDF Project Page [BibTex]


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From Monocular SLAM to Autonomous Drone Exploration

von Stumberg, L., Usenko, V., Engel, J., Stueckler, J., Cremers, D.

In European Conference on Mobile Robots (ECMR), September 2017 (inproceedings)

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[BibTex]

[BibTex]


Coupling Adaptive Batch Sizes with Learning Rates
Coupling Adaptive Batch Sizes with Learning Rates

Balles, L., Romero, J., Hennig, P.

In Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017 (inproceedings)

Abstract
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On three image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.

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Code link (url) Project Page [BibTex]

Code link (url) Project Page [BibTex]


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Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (conference)

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DOI [BibTex]

DOI [BibTex]


Locomotion of light-driven soft microrobots through a hydrogel via local melting
Locomotion of light-driven soft microrobots through a hydrogel via local melting

Palagi, S., Mark, A. G., Melde, K., Qiu, T., Zeng, H., Parmeggiani, C., Martella, D., Wiersma, D. S., Fischer, P.

In 2017 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), pages: 1-5, July 2017 (inproceedings)

Abstract
Soft mobile microrobots whose deformation can be directly controlled by an external field can adapt to move in different environments. This is the case for the light-driven microrobots based on liquid-crystal elastomers (LCEs). Here we show that the soft microrobots can move through an agarose hydrogel by means of light-controlled travelling-wave motions. This is achieved by exploiting the inherent rise of the LCE temperature above the melting temperature of the agarose gel, which facilitates penetration of the microrobot through the hydrogel. The locomotion performance is investigated as a function of the travelling-wave parameters, showing that effective propulsion can be obtained by adapting the generated motion to the specific environmental conditions.

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DOI [BibTex]

DOI [BibTex]


Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 5295-5301, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv DOI Project Page [BibTex]

PDF arXiv DOI Project Page [BibTex]


Virtual vs. {R}eal: Trading Off Simulations and Physical Experiments in Reinforcement Learning with {B}ayesian Optimization
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization

Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A. P., Krause, A., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 1557-1563, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

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PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]

PDF arXiv ICRA 2017 Spotlight presentation Virtual vs. Real - Video explanation DOI Project Page [BibTex]


Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 54, pages: 528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (conference)

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pdf link (url) Project Page [BibTex]

pdf link (url) Project Page [BibTex]


Wireless micro-robots for endoscopic applications in urology
Wireless micro-robots for endoscopic applications in urology

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

In Eur Urol Suppl, 16(3):e1914, March 2017 (inproceedings)

Abstract
Endoscopy is an essential and common method for both diagnostics and therapy in Urology. Current flexible endoscope is normally cable-driven, thus it is hard to be miniaturized and its reachability is restricted as only one bending section near the tip with one degree of freedom (DoF) is allowed. Recent progresses in micro-robotics offer a unique opportunity for medical inspections in minimally invasive surgery. Micro-robots are active devices that has a feature size smaller than one millimeter and can normally be actuated and controlled wirelessly. Magnetically actuated micro-robots have been demonstrated to propel through biological fluids.Here, we report a novel micro robotic arm, which is actuated wirelessly by ultrasound. It works as a miniaturized endoscope with a side length of ~1 mm, which fits through the 3 Fr. tool channel of a cystoscope, and successfully performs an active cystoscopy in a rabbit bladder.

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link (url) DOI [BibTex]


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New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.

Dagstuhl Reports, 6(11):142-167, 2017 (book)

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DOI [BibTex]

DOI [BibTex]


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Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Ma, L., Stueckler, J., Kerl, C., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Accurate depth and normal maps from occlusion-aware focal stack symmetry

Strecke, M., Alperovich, A., Goldluecke, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Semi-Supervised Deep Learning for Monocular Depth Map Prediction

Kuznietsov, Y., Stueckler, J., Leibe, B.

In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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[BibTex]

[BibTex]


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Shadow and Specularity Priors for Intrinsic Light Field Decomposition

Alperovich, A., Johannsen, O., Strecke, M., Goldluecke, B.

In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Keyframe-Based Visual-Inertial Online SLAM with Relocalization

Kasyanov, A., Engelmann, F., Stueckler, J., Leibe, B.

In IEEE/RSJ Int. Conference on Intelligent Robots and Systems, IROS, 2017 (inproceedings)

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[BibTex]

[BibTex]


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SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction

Engelmann, F., Stueckler, J., Leibe, B.

In IEEE Winter Conference on Applications of Computer Vision, WACV, 2017 (inproceedings)

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[BibTex]

[BibTex]

2010


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Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy

Bangert, M., Hennig, P., Oelfke, U.

In pages: 746-751 , (Editors: Draghici, S. , T.M. Khoshgoftaar, V. Palade, W. Pedrycz, M.A. Wani, X. Zhu), IEEE, Piscataway, NJ, USA, Ninth International Conference on Machine Learning and Applications (ICMLA), December 2010 (inproceedings)

Abstract
We present a method for fully automated selection of treatment beam ensembles for external radiation therapy. We reformulate the beam angle selection problem as a clustering problem of locally ideal beam orientations distributed on the unit sphere. For this purpose we construct an infinite mixture of von Mises-Fisher distributions, which is suited in general for density estimation from data on the D-dimensional sphere. Using a nonparametric Dirichlet process prior, our model infers probability distributions over both the number of clusters and their parameter values. We describe an efficient Markov chain Monte Carlo inference algorithm for posterior inference from experimental data in this model. The performance of the suggested beam angle selection framework is illustrated for one intra-cranial, pancreas, and prostate case each. The infinite von Mises-Fisher mixture model (iMFMM) creates between 18 and 32 clusters, depending on the patient anatomy. This suggests to use the iMFMM directly for beam ensemble selection in robotic radio surgery, or to generate low-dimensional input for both subsequent optimization of trajectories for arc therapy and beam ensemble selection for conventional radiation therapy.

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Web DOI [BibTex]

2010


Web DOI [BibTex]


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Coherent Inference on Optimal Play in Game Trees

Hennig, P., Stern, D., Graepel, T.

In JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, pages: 326-333, (Editors: Teh, Y.W. , M. Titterington ), JMLR, Cambridge, MA, USA, Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

Abstract
Round-based games are an instance of discrete planning problems. Some of the best contemporary game tree search algorithms use random roll-outs as data. Relying on a good policy, they learn on-policy values by propagating information upwards in the tree, but not between sibling nodes. Here, we present a generative model and a corresponding approximate message passing scheme for inference on the optimal, off-policy value of nodes in smooth AND/OR trees, given random roll-outs. The crucial insight is that the distribution of values in game trees is not completely arbitrary. We define a generative model of the on-policy values using a latent score for each state, representing the value under the random roll-out policy. Inference on the values under the optimal policy separates into an inductive, pre-data step and a deductive, post-data part. Both can be solved approximately with Expectation Propagation, allowing off-policy value inference for any node in the (exponentially big) tree in linear time.

ei pn

PDF Web [BibTex]

PDF Web [BibTex]


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Accelerometer-based Tilt Estimation of a Rigid Body with only Rotational Degrees of Freedom

Trimpe, S., D’Andrea, R.

In Proceedings of the IEEE International Conference on Robotics and Automation, 2010 (inproceedings)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Combining depth and color cues for scale- and viewpoint-invariant object segmentation and recognition using Random Forests

Stueckler, J., Behnke, S.

In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages: 4566-4571, October 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Intuitive Multimodal Interaction for Domestic Service Robots

Nieuwenhuisen, M., Stueckler, J., Behnke, S.

In Proc. of the ISR/ROBOTIK, VDE Verlag, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Absence of element specific ferromagnetism in Co doped ZnO investigated by soft X-ray resonant reflectivity

Goering, E., Brück, S., Tietze, T., Jakob, G., Gacic, M., Adrian, H.

In 200, Glasgow, Scotland, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Probing the local magnetization dynamics in large systems with spatial inhomogeneity

Li, J, Lee, M.-S., Amaladass, E., He, W., Eimüller, T.

In 200, Glasgow, Scotland, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Wetting of grain boundaries in Al by the solid Al3Mg2 phase

Straumal, B. B., Baretzky, B., Kogtenkova, O. A., Straumal, A. B., Sidorenko, A. S.

In 45, pages: 2057-2061, Athens, Greek, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Damping of near-adiabatic magnetization dynamics by excitations of electron-hole pairs

Seib, J., Steiauf, D., Fähnle, M.

In 200, Karlsruhe, Germany, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Improving People Awareness of Service Robots by Semantic Scene Knowledge

Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 157-168, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Magnetization reversal of Fe/Gd multilayers on self-assembled arrays of nanospheres

Amaladass, E., Eimüller, T., Ludescher, B., Tyliszczak, T., Schütz, G.

In 200, Glasgow, Scotland, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Towards Semantic Scene Analysis with Time-of-flight Cameras

Holz, D., Schnabel, R., Droeschel, D., Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 121-132, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Contact angles by the solid-phase grain boundary wetting (coverage) in the Co-Cu system

Straumal, B. B., Kogtenkova, O. A., Straumal, A. B., Kuchyeyev, Y. O., Baretzky, B.

In 45, pages: 4271-4275, Glasgow, Scotland, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization

Schulz, H., Liu, W., Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 397-408, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Improving indoor navigation of autonomous robots by an explicit representation of doors

Nieuwenhuisen, M., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 4895-4901, May 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Unusual super-ductility at room temperature in an ultrafine-grained aluminum alloy

Valiev, R. Z., Murashkin, M. Y., Kilmametov, A., Straumal, B., Chinh, N. Q., Langdon, T.

In 45, pages: 4718-4724, Seattle, WA, USA, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Demagnetization on the fs time-scale by the Elliott-Yafet mechanism

Steiauf, D., Illg, C., Fähnle, M.

In 200, Karlsruhe, Germany, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Improving imitated grasping motions through interactive expected deviation learning

Gräve, K., Stueckler, J., Behnke, S.

In Proc. of the 10th IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 397-404, December 2010 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Motion Skills from Expert Demonstrations and Own Experience using Gaussian Process Regression

Gräve, K., Stueckler, J., Behnke, S.

In Proc. of the ISR/ROBOTIK, pages: 1-8, VDE Verlag, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Using Time-of-Flight cameras with active gaze control for 3D collision avoidance

Droeschel, D., Holz, D., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 4035-4040, May 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Handbook of Hydrogen Storage

Hirscher, M.

pages: 353 p., Wiley-VCH, Weinheim, 2010 (book)

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[BibTex]

[BibTex]


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The X-ray microscopy beamline UE46-PGM2 at BESSY

Follath, R., Schmidt, J. S., Weigand, M., Fauth, K.

In 10th International Conference on Synchrotron Radiation Instrumentation, 1234, pages: 323-326, AIP Conference Proceedings, American Institute of Physics, Melbourne, Australia, 2010 (inproceedings)

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DOI [BibTex]

DOI [BibTex]

2008


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Wetting and premelting of triple junctions and grain boundaries in the Al-Zn alloys

Straumal, B., Kogtenkova, O., Protasova, S., Mazilkin, A., Zieba, P., Czeppe, T., Wojewoda-Budka, J., Faryna, M.

In 495, pages: 126-131, Alicante, Spain, 2008 (inproceedings)

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DOI [BibTex]

2008


DOI [BibTex]


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In-lane Localization in Road Networks using Curbs Detected in Omnidirectional Height Images

Stueckler, J., Schulz, H., Behnke, S.

In Proceedings of Robotik 2008, 2008 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Orthogonal wall correction for visual motion estimation

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 1-6, May 2008 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Study of the intermixing of Fe\textendashPt multilayers by analytical and high-resolution transmission electron microscopy

Sigle, W., Kaiser, T., Goll, D., Goo, N. H., Srot, V., van Aken, P. A., Detemple, E., Jäger, W.

In EMC2008, 14th European Microscopy Congress, Vol. 2: Materials Science, pages: 109-110, Springer, Aachen, Germany, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]

2007


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Less Conservative Polytopic LPV Models for Charge Control by Combining Parameter Set Mapping and Set Intersection

Kwiatkowski, A., Trimpe, S., Werner, H.

In Proceedings of the 46th IEEE Conference on Decision and Control, 2007 (inproceedings)

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DOI [BibTex]

2007


DOI [BibTex]


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Hierarchical reactive control for a team of humanoid soccer robots

Behnke, S., Stueckler, J., Schreiber, M., Schulz, H., Böhnert, M., Meier, K.

In Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 622-629, November 2007 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2006


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See, walk, and kick: Humanoid robots start to play soccer

Behnke, S., Schreiber, M., Stueckler, J., Renner, R., Strasdat, H.

In Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pages: 497-503, December 2006 (inproceedings)

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link (url) DOI [BibTex]

2006


link (url) DOI [BibTex]


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Ab-initio calculations: I. Basic principles of the density functional electron theory and combination with phenomenological theories

Fähnle, M.

In Structural defects in ordered alloys and intermetallics. Characterization and modelling, pages: IX-1-IX-10, COST and CNRS, Bonascre [Ariege, France], 2006 (inproceedings)

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[BibTex]

[BibTex]


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Hard magnetic FePt thin films and nanostructures in L1(0) phases

Goll, D., Breitling, A., Goo, N. H., Sigle, W., Hirscher, M., Schütz, G.

In 13, pages: 97-101, Beijing, PR China, 2006 (inproceedings)

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[BibTex]

[BibTex]


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Ab-initio calculations: II. Application to atomic defects, phase diagrams, dislocations

Fähnle, M.

In Structural defects in ordered alloys and intermetallics. Characterization and modelling, pages: XIV-1-XIV-11, COST and CNRS, Bonascre [Ariege, France], 2006 (inproceedings)

mms

[BibTex]

[BibTex]