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2018


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Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

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arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


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Gait learning for soft microrobots controlled by light fields

Rohr, A. V., Trimpe, S., Marco, A., Fischer, P., Palagi, S.

In International Conference on Intelligent Robots and Systems (IROS) 2018, pages: 6199-6206, International Conference on Intelligent Robots and Systems 2018, October 2018 (inproceedings)

Abstract
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing environments. However, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is highly data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semisynthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot’s locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on lightcontrolled soft microrobots and probabilistic learning control.

ics pf

arXiv IEEE Xplore DOI Project Page [BibTex]

arXiv IEEE Xplore DOI Project Page [BibTex]


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On the Integration of Optical Flow and Action Recognition

Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, M. J.

In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 281-297, Springer, Cham, October 2018 (inproceedings)

Abstract
Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: 1) optical flow is useful for action recognition because it is invariant to appearance, 2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, 3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, 4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and 5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

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arXiv DOI [BibTex]

arXiv DOI [BibTex]


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Towards Robust Visual Odometry with a Multi-Camera System

Liu, P., Geppert, M., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.

In International Conference on Intelligent Robots and Systems (IROS) 2018, International Conference on Intelligent Robots and Systems, October 2018 (inproceedings)

Abstract
We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window optimizer is used to refine poses and structure jointly. Our formulation is flexible enough to support an arbitrary number of stereo cameras. We evaluate our algorithm thoroughly on five datasets. The datasets were captured in different conditions: daytime, night-time with near-infrared (NIR) illumination and night-time without NIR illumination. Experimental results show that a multi-camera setup makes the VO more robust to challenging environments, especially night-time conditions, in which a single stereo configuration fails easily due to the lack of features.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Learning Priors for Semantic 3D Reconstruction

Cherabier, I., Schönberger, J., Oswald, M., Pollefeys, M., Geiger, A.

In Computer Vision – ECCV 2018, Springer International Publishing, Cham, September 2018 (inproceedings)

Abstract
We present a novel semantic 3D reconstruction framework which embeds variational regularization into a neural network. Our network performs a fixed number of unrolled multi-scale optimization iterations with shared interaction weights. In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry. Compared to previous learning-based approaches to 3D reconstruction, we integrate powerful long-range dependencies using variational coarse-to-fine optimization. As a result, our network architecture requires only a moderate number of parameters while keeping a high level of expressiveness which enables learning from very little data. Experiments on real and synthetic datasets demonstrate that our network achieves higher accuracy compared to a purely variational approach while at the same time requiring two orders of magnitude less iterations to converge. Moreover, our approach handles ten times more semantic class labels using the same computational resources.

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pdf suppmat Project Page Video DOI Project Page [BibTex]

pdf suppmat Project Page Video DOI Project Page [BibTex]


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

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pdf suppmat Video Project Page DOI Project Page [BibTex]

pdf suppmat Video Project Page DOI Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

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Project Page [BibTex]

Project Page [BibTex]


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SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images

Coors, B., Condurache, A. P., Geiger, A.

European Conference on Computer Vision (ECCV), September 2018 (conference)

Abstract
Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of view is essential, such as in virtual reality applications or in autonomous robots. Unfortunately, standard convolutional neural networks are not well suited for this scenario as the natural projection surface is a sphere which cannot be unwrapped to a plane without introducing significant distortions, particularly in the polar regions. In this work, we present SphereNet, a novel deep learning framework which encodes invariance against such distortions explicitly into convolutional neural networks. Towards this goal, SphereNet adapts the sampling locations of the convolutional filters, effectively reversing distortions, and wraps the filters around the sphere. By building on regular convolutions, SphereNet enables the transfer of existing perspective convolutional neural network models to the omnidirectional case. We demonstrate the effectiveness of our method on the tasks of image classification and object detection, exploiting two newly created semi-synthetic and real-world omnidirectional datasets.

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pdf suppmat Project Page [BibTex]


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Soft Miniaturized Linear Actuators Wirelessly Powered by Rotating Permanent Magnets

Qiu, T., Palagi, S., Sachs, J., Fischer, P.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 3595-3600, May 2018 (inproceedings)

Abstract
Wireless actuation by magnetic fields allows for the operation of untethered miniaturized devices, e.g. in biomedical applications. Nevertheless, generating large controlled forces over relatively large distances is challenging. Magnetic torques are easier to generate and control, but they are not always suitable for the tasks at hand. Moreover, strong magnetic fields are required to generate a sufficient torque, which are difficult to achieve with electromagnets. Here, we demonstrate a soft miniaturized actuator that transforms an externally applied magnetic torque into a controlled linear force. We report the design, fabrication and characterization of both the actuator and the magnetic field generator. We show that the magnet assembly, which is based on a set of rotating permanent magnets, can generate strong controlled oscillating fields over a relatively large workspace. The actuator, which is 3D-printed, can lift a load of more than 40 times its weight. Finally, we show that the actuator can be further miniaturized, paving the way towards strong, wirelessly powered microactuators.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Robust Dense Mapping for Large-Scale Dynamic Environments

Barsan, I. A., Liu, P., Pollefeys, M., Geiger, A.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

Abstract
We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work.

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pdf Video Project Page Project Page [BibTex]

pdf Video Project Page Project Page [BibTex]


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RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials

Paschalidou, D., Ulusoy, A. O., Schmitt, C., Gool, L., Geiger, A.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)

Abstract
In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occlusion. Instead, classical approaches based on Markov Random Fields (MRF) with ray-potentials explicitly model these physical processes, but they cannot cope with large surface appearance variations across different viewpoints. In this paper, we propose RayNet, which combines the strengths of both frameworks. RayNet integrates a CNN that learns view-invariant feature representations with an MRF that explicitly encodes the physics of perspective projection and occlusion. We train RayNet end-to-end using empirical risk minimization. We thoroughly evaluate our approach on challenging real-world datasets and demonstrate its benefits over a piece-wise trained baseline, hand-crafted models as well as other learning-based approaches.

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pdf suppmat Video Project Page code Poster Project Page [BibTex]

pdf suppmat Video Project Page code Poster Project Page [BibTex]


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Learning to select computations

Callaway, F., Gul, S., Krueger, P., Griffiths, T. L., Lieder, F.

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Deep Marching Cubes: Learning Explicit Surface Representations

Liao, Y., Donne, S., Geiger, A.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)

Abstract
Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (eg, TSDF) from which 3D surface meshes must be extracted in a post-processing step (eg, via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss functions which allow for training our model with sparse point supervision. Our experiments demonstrate that the model allows for predicting sub-voxel accurate 3D shapes of arbitrary topology. Additionally, it learns to complete shapes and to separate an object's inside from its outside even in the presence of sparse and incomplete ground truth. We investigate the benefits of our approach on the task of inferring shapes from 3D point clouds. Our model is flexible and can be combined with a variety of shape encoder and shape inference techniques.

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pdf suppmat Video Project Page Poster Project Page [BibTex]

pdf suppmat Video Project Page Poster Project Page [BibTex]


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Semantic Visual Localization

Schönberger, J., Pollefeys, M., Geiger, A., Sattler, T.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)

Abstract
Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, eg, in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting 3D descriptors are robust to missing observations by encoding high-level 3D geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes.

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pdf suppmat Poster Project Page [BibTex]

pdf suppmat Poster Project Page [BibTex]


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Which Training Methods for GANs do actually Converge?

Mescheder, L., Geiger, A., Nowozin, S.

International Conference on Machine learning (ICML), 2018 (conference)

Abstract
Recent work has shown local convergence of GAN training for absolutely continuous data and generator distributions. In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent. Furthermore, we discuss regularization strategies that were recently proposed to stabilize GAN training. Our analysis shows that GAN training with instance noise or zero-centered gradient penalties converges. On the other hand, we show that Wasserstein-GANs and WGAN-GP with a finite number of discriminator updates per generator update do not always converge to the equilibrium point. We discuss these results, leading us to a new explanation for the stability problems of GAN training. Based on our analysis, we extend our convergence results to more general GANs and prove local convergence for simplified gradient penalties even if the generator and data distributions lie on lower dimensional manifolds. We find these penalties to work well in practice and use them to learn high-resolution generative image models for a variety of datasets with little hyperparameter tuning.

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code video paper supplement slides poster Project Page [BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


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Learning 3D Shape Completion from Laser Scan Data with Weak Supervision

Stutz, D., Geiger, A.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)

Abstract
3D shape completion from partial point clouds is a fundamental problem in computer vision and computer graphics. Recent approaches can be characterized as either data-driven or learning-based. Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations. Learning-based approaches, in contrast, avoid the expensive optimization step and instead directly predict the complete shape from the incomplete observations using deep neural networks. However, full supervision is required which is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, ie, learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. Tackling 3D shape completion of cars on ShapeNet and KITTI, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and a state-of-the-art data-driven approach while being significantly faster. On ModelNet, we additionally show that the approach is able to generalize to other object categories as well.

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pdf suppmat Project Page Poster Project Page [BibTex]

pdf suppmat Project Page Poster Project Page [BibTex]


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Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Learning Transformation Invariant Representations with Weak Supervision

Coors, B., Condurache, A., Mertins, A., Geiger, A.

In International Conference on Computer Vision Theory and Applications, International Conference on Computer Vision Theory and Applications, 2018 (inproceedings)

Abstract
Deep convolutional neural networks are the current state-of-the-art solution to many computer vision tasks. However, their ability to handle large global and local image transformations is limited. Consequently, extensive data augmentation is often utilized to incorporate prior knowledge about desired invariances to geometric transformations such as rotations or scale changes. In this work, we combine data augmentation with an unsupervised loss which enforces similarity between the predictions of augmented copies of an input sample. Our loss acts as an effective regularizer which facilitates the learning of transformation invariant representations. We investigate the effectiveness of the proposed similarity loss on rotated MNIST and the German Traffic Sign Recognition Benchmark (GTSRB) in the context of different classification models including ladder networks. Our experiments demonstrate improvements with respect to the standard data augmentation approach for supervised and semi-supervised learning tasks, in particular in the presence of little annotated data. In addition, we analyze the performance of the proposed approach with respect to its hyperparameters, including the strength of the regularization as well as the layer where representation similarity is enforced.

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pdf [BibTex]

pdf [BibTex]


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Learning equations for extrapolation and control

Sahoo, S. S., Lampert, C. H., Martius, G.

In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

Abstract
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

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Code Arxiv Poster Slides link (url) Project Page [BibTex]

Code Arxiv Poster Slides link (url) Project Page [BibTex]


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Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Sun, H., Martius, G.

Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

Abstract
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Direct observations of sub-100 nm spin wave propagation in magnonic wave-guides

Träger, N., Gruszecki, P., Lisiecki, F., Förster, J., Weigand, M., Kuswik, P., Dubowik, J., Schütz, G., Krawczyk, M., Gräfe, J.

In 2018 IEEE International Magnetics Conference (INTERMAG 2018), IEEE, Singapore, 2018 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Interpreting FORC diagrams beyond the Preisach model: an experimental permalloy micro array investigation

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

In 2018 IEEE International Magnetics Conference (INTERMAG 2018), IEEE, Singapore, 2018 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]

2001


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Computational micromagnetism of magnetic structures and magnetization processes in thin plantelets and small particles

Kronmüller, H., Hertel, R.

In Magnetic Storage Sstems Beyond 2000, 41, pages: 345-362, Nato Science Series II: Mathematics, Physics and Chemistry, Kluwer Academic Publishers, Rhodos, Greece, 2001 (inproceedings)

mms

[BibTex]

2001


[BibTex]


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Hydrogen storage in mechanically treated single wall carbon nanotrubes

Haluska, M., Hulman, M., Hirscher, M., Becher, M., Roth, S., Stepanek, I., Bernier, P.

In Electronic Properties of Molecular Nanostructures: XV International Winterschool/Euroconference, 591, pages: 603-608, American Institute of Physics Conference Proceedings, AIP, Kirchberg [Austria], 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Isotopic mass and lattice constant of Si and Ge: X-Ray standing wave measurements

Zegenhagen, J., Kazimirov, A., Cao, L. X., Konuma, M., Sozontov, E., Plachke, D., Carstanjen, H. D., Bilger, G., Haller, E., Kohn, V., Cardona, M.

In Proceedings of the 25th Conference on the Physics of Semiconductors, 87, pages: 125-127, Springer proceedings in physics, Springer, Osaka, Japan, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Positron Annihilation Studies on Stable and Undercooled Metal Melts at the Stuttgart Pelletron

Stoll, H., Siegle, A., Major, J.

In Application of Accelerators in Research and Industry, 576, pages: 749-752, AIP Conference Proceedings, Denton, Texas, USA, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Submicrometer spatially resolved measurements of mechanical properties and correlation to microstructure and composition

Kunert, M., Baretzky, B., Baker, S. P., Mittemeijer, E. J.

In Fundamentals of Nanoindentation and Nanotribology II, 649, pages: Q3.2.1-Q3.2.6, Materials Research Society Symposium Proceedings, MRS, Boston, MA, USA, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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The six-jump diffusion cycles in B2-compounds

Drautz, R., Meyer, B., Fähnle, M.

In Proceedings of DIMAT 2000, the Fifth International Conference on Diffusion in Materials, pages: 417-422, Defect and Diffusion Forum, Scitec Publications Ltd., Paris, France, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Ionic nitriding of austenitic and ferritic steel with the aid of a high aperture hall current accelerator

Straumal, B. B., Vershinin, N. F., Friesel, M., Ishenko, S. A., Gust, W.

In Diffusion in Materials DIMAT2000, 194, pages: 1457-1462, Defect and Diffusion Forum, Trans Tech, Paris, France, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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First proof of slow trapping of positronium in polymers by an Age-Momentum-Correlation (AMOC) experiment

Dauwe, C., Balcaen, N., van Waeyenberge, B., van Petegem, S., Stoll, H.

In Positron Annihilation. Proceedings of the 12th International Conference on Positron Annihilation, 363/365, pages: 254-256, Materials Science Forum, Trans Tech Publications Ltd., München, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Positron-age-momentum correlation

Stoll, H., Bandzuch, P., Siegle, A.

In Positron Annihilation: Proceedings of the 12th International Conference on Positron Annihilation, 363-365, pages: 547-551, Materials Science Forum, Trans Tech Publications Ltd., München, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Nanocrystalline and nanostructured high-performance permanent magnets

Goll, D., Hadjipanayis, G. C., Kronmüller, H.

In Applications of Ferromagnetic and Optical Materials, Storage and Magnetoelectronics, 674, pages: U2.4.1-U2.4.12, Materials Research Society Symposium Proceedings, MRS, San Francisco, Calif., 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Ion beam analysis with monolayer depth resolution using the electrostatic spectrometer at the MPI Stuttgart

Plachke, D., Blohm, G., Fischer, T., Khellaf, A., Kruse, O., Stoll, H., Carstanjen, H. D.

In Proceedings of the 16th International Conference on Applications of Accelerators in Research and Industry, 576, pages: 458-462, American Institute of Physics Conference Proceedings, AIP, Denton, Texas, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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From the electronic structure to the macroscopiy behavior: A multi-scale analysis of plasticity in intermetallic compounds

Fähnle, M., Kohlhammer, S., Bester, G.

In Influences of Interface and Dislocation Behavior on Microstructure Evolution, 652, pages: Y4.5.1.-Y4.5.12, Materials Research Society Symposium Proceedings, MRS, Boston, Mass., USA, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Influence of the microstructure on the magnetic properties of giant-magnetostrictive TbDyFe films

Hirscher, M., Winzek, B., Fischer, S. F., Kronmüller, H.

In Smart Materials. Proceedings of the 1st Caesarium, pages: 23-37, Springer, Bonn, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Materials analysis with monolayer depth resolution using MeV ion beams

Carstanjen, H. D.

In 117, Las Vegas, USA, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Flux-line pinning in low-angle grain boundaries.

Albrecht, J., Leonhardt, S., Kronmüller, H.

In Proceedings 10th International Workshop on Critical Currents (IWCC 2001), pages: 41-43, Göttingen, Germany, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Measurement of the low-temperature self-diffusivity of lithium by elastic recoil detection analysis

Wieland, O., Carstanjen, H. D.

In Proceedings of DIMAT 2000, the Fifth International Conference on Diffusion in Materials, 194/199, pages: 35-41, Defect and Diffusion Forum, Scitec Publications Ltd., Paris, France, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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From the electronic structure to the macroscopic behaviour: a multi-scale analysis of plasticity in intermetallic compounds

Fähnle, M., Kohlhammer, S., Bester, G.

In Influences of Interface and Dislocation Behavior on Microstructure Evolution, 652, pages: Y.4.5.1-Y.4.5.12, Materials Research Society Symposium Proceedings, MRS, Boston, Mass., 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Enhancement of the critical current density of YBa2Cu3O7-8-films by substracte irradiation

Leonhardt, S., Albrecht, J., Warthmann, R., Kronmüller, H.

In High-Tc Superconductors and Related Applications: Materials Science, Fundamental Properties, and Some Future Electronic Applications. Proceedings of the NATO Advanced Study Institute, 86, pages: 529-534, NATO Science Series 3. High Technology, Kluwer Academic Publishers, Albena, Bulgaria, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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AMOC studies of positronium in fine MgO powder

van Waeyenberge, B., Dauwe, C., Stoll, H.

In Positron Annihilation. Proceedings of the 12th International Conference on Positron Annihilation, 363/365, pages: 401-403, Materials Science Forum, Trans Tech Publications Ltd., München, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Atomic defects and electronic structure of B2-FeAl, CoAl and NiAl

Fähnle, M., Meyer, B., Bester, G., Majer, J., Börnsen, N.

In Proceedings of DIMAT 2000, the Fifth International Conference on Diffusion in Materials, 194/199, pages: 279-285, Defect and Diffusion Forum, Scitec Publications Ltd., Paris, France, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Geometric Image Synthesis

Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C.

(conference)

avg

Project Page [BibTex]


Project Page [BibTex]