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Perceiving Systems Ph.D. Thesis Towards Geometric Understanding of Motion Ranjan, A. University of Tübingen, December 2019
<p> The motion of the world is inherently dependent on the spatial structure of the world and its geometry. Therefore, classical optical flow methods try to model this geometry to solve for the motion. However, recent deep learning methods take a completely different approach. They try to predict optical flow by learning from labelled data. Although deep networks have shown state-of-the-art performance on classification problems in computer vision, they have not been as effective in solving optical flow. The key reason is that deep learning methods do not explicitly model the structure of the world in a neural network, and instead expect the network to learn about the structure from data. We hypothesize that it is difficult for a network to learn about motion without any constraint on the structure of the world. Therefore, we explore several approaches to explicitly model the geometry of the world and its spatial structure in deep neural networks. </p> <p> The spatial structure in images can be captured by representing it at multiple scales. To represent multiple scales of images in deep neural nets, we introduce a Spatial Pyramid Network (SpyNet). Such a network can leverage global information for estimating large motions and local information for estimating small motions. We show that SpyNet significantly improves over previous optical flow networks while also being the smallest and fastest neural network for motion estimation. SPyNet achieves a 97% reduction in model parameters over previous methods and is more accurate. </p> <p> The spatial structure of the world extends to people and their motion. Humans have a very well-defined structure, and this information is useful in estimating optical flow for humans. To leverage this information, we create a synthetic dataset for human optical flow using a statistical human body model and motion capture sequences. We use this dataset to train deep networks and see significant improvement in the ability of the networks to estimate human optical flow. </p> <p> The structure and geometry of the world affects the motion. Therefore, learning about the structure of the scene together with the motion can benefit both problems. To facilitate this, we introduce Competitive Collaboration, where several neural networks are constrained by geometry and can jointly learn about structure and motion in the scene without any labels. To this end, we show that jointly learning single view depth prediction, camera motion, optical flow and motion segmentation using Competitive Collaboration achieves state-of-the-art results among unsupervised approaches. </p> <p> Our findings provide support for our hypothesis that explicit constraints on structure and geometry of the world lead to better methods for motion estimation. </p>
PhD Thesis BibTeX
Micro, Nano, and Molecular Systems Article HPLC of monolayer-protected Gold clusters with baseline separation Knoppe, S., Vogt, P. Analytical Chemistry, 91:1603, December 2019
The properties of ultrasmall metal nanoparticles (ca. 10–200 metal atoms), or monolayer-protected metal clusters (MPCs), drastically depend on their atomic structure. For systematic characterization and application, assessment of their purity is of high importance. Currently, the gold standard for purity control of MPCs is mass spectrometry (MS). Mass spectrometry, however, cannot always detect small impurities; MS of certain clusters, for example, ESI-TOF of Au40(SR)24, is not successful at all. We here present a simple reversed-phase HPLC method for purity control of a series of small alkanethiolate-protected gold clusters. The method allows the detection of small impurities with high sensitivity. Linear correlation between alkyl chain length of Au25(SC_n H_(2n+1))18 clusters (n = 6, 8, 10, 12) and their retention time was noticed.
DOI URL BibTeX
Empirical Inference Conference Paper Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A. NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (Published) arXiv Poster URL BibTeX
Statistical Learning Theory Conference Paper Foundations of Comparison-Based Hierarchical Clustering Ghoshdastidar, D., Perrot, M., von Luxburg, U. Advances in Neural Information Processing Systems 32 (NIPS 2019), NeurIPS, Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Autonomous Learning Conference Paper Assessing Aesthetics of Generated Abstract Images Using Correlation Structure Khajehabdollahi, S., Martius, G., Levina, A. In Proceedings 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 306-313, IEEE, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), December 2019 Arxiv DOI BibTeX
Empirical Inference Conference Paper Fisher Efficient Inference of Intractable Models Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Micro, Nano, and Molecular Systems Modern Magnetic Systems Article Soft-magnetic coatings as possible sensors for magnetic imaging of superconductors Ionescu, A., Simmendinger, J., Bihler, M., Miksch, C., Fischer, P., Soltan, S., Schütz, G., Albrecht, J. Supercond. Sci. and Tech., 33:015002, IOP, December 2019
Magnetic imaging of superconductors typically requires a soft-magnetic material placed on top of the superconductor to probe local magnetic fields. For reasonable results the influence of the magnet onto the superconductor has to be small. Thin YBCO films with soft-magnetic coatings are investigated using SQUID magnetometry. Detailed measurements of the magnetic moment as a function of temperature, magnetic field and time have been performed for different heterostructures. It is found that the modification of the superconducting transport in these heterostructures strongly depends on the magnetic and structural properties of the soft-magnetic material. This effect is especially pronounced for an inhomogeneous coating consisting of ferromagnetic nanoparticles.
DOI URL BibTeX
Theory of Inhomogeneous Condensed Matter Article Using the fluctuation-dissipation theorem for nonconservative forces Asheichyk, K., Krüger, M. Physical Review Research, 1(3):033151, American Physical Society (APS), College Park, Maryland, United States, December 2019 (Published) DOI BibTeX
Empirical Inference Conference Paper A Model to Search for Synthesizable Molecules Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., Hernández-Lobato, J. M. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 7935-7947, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Are Disentangled Representations Helpful for Abstract Visual Reasoning? van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 14222-14235, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Autonomous Learning Conference Paper Control What You Can: Intrinsically Motivated Task-Planning Agent Blaes, S., Vlastelica, M., Zhu, J., Martius, G. In Advances in Neural Information Processing Systems (NeurIPS 2019), 12520-12531, Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Accepted)
We present a novel intrinsically motivated agent that learns how to control the environment in the fastest possible manner by optimizing learning progress. It learns what can be controlled, how to allocate time and attention, and the relations between objects using surprise based motivation. The effectiveness of our method is demonstrated in a synthetic as well as a robotic manipulation environment yielding considerably improved performance and smaller sample complexity. In a nutshell, our work combines several task-level planning agent structures (backtracking search on task graph, probabilistic road-maps, allocation of search efforts) with intrinsic motivation to achieve learning from scratch.
PDF Supplementary material NeurIPS Page Project Page Video Poster BibTeX
Intelligent Control Systems Conference Paper Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources Haksar, R., Solowjow, F., Trimpe, S., Schwager, M. In Proceedings of the 58th IEEE International Conference on Decision and Control (CDC) , 1315-1322, 58th IEEE International Conference on Decision and Control (CDC), December 2019 (Published) PDF BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Convergence Guarantees for Adaptive Bayesian Quadrature Methods Kanagawa, M., Hennig, P. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Movement Generation and Control Article Epstein, Project Maven, and Some Reasons to Think About Where We Get Our Funding [Ethical, Legal, and Societal Issues] Bretl, T., Righetti, L., Madhavan, R. IEEE Robotics & Automation Magazine, 26, December 2019 BibTeX
Empirical Inference Conference Paper Exploiting the modularity of deep networks to generate visual counterfactuals Besserve, M., Mehrjou, A., Sun, R., Schölkopf, B. NeurIPS 2019 - Workshop on Shared Visual Representations in Human & Machine Intelligence, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Flex-Convolution Groh*, F., Wieschollek*, P., Lensch, H. P. A. Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, 11361:105-122, Lecture Notes in Computer Science, (Editors: Jawahar, C. V. and Li, Hongdong and Mori, Greg and Schindler, Konrad), Springer International Publishing, December 2019, *equal contribution (Published) DOI BibTeX
Article Growing the Humanoid Robotics Community Righetti, L., Sugihara, T., Metta, G., Yamane, K. IEEE Robotics & Automation Magazine, 26:136-137, December 2019 (Published) BibTeX
Haptic Intelligence Article Hierarchical Task-Parameterized Learning from Demonstration for Collaborative Object Movement Hu, S., Kuchenbecker, K. J. Applied Bionics and Biomechanics, 2019(9765383):1-25, December 2019 (Published)
Learning from demonstration (LfD) enables a robot to emulate natural human movement instead of merely executing preprogrammed behaviors. This article presents a hierarchical LfD structure of task-parameterized models for object movement tasks, which are ubiquitous in everyday life and could benefit from robotic support. Our approach uses the task-parameterized Gaussian mixture model (TP-GMM) algorithm to encode sets of demonstrations in separate models that each correspond to a different task situation. The robot then maximizes its expected performance in a new situation by either selecting a good existing model or requesting new demonstrations. Compared to a standard implementation that encodes all demonstrations together for all test situations, the proposed approach offers four advantages. First, a simply defined distance function can be used to estimate test performance by calculating the similarity between a test situation and the existing models. Second, the proposed approach can improve generalization, e.g., better satisfying the demonstrated task constraints and speeding up task execution. Third, because the hierarchical structure encodes each demonstrated situation individually, a wider range of task situations can be modeled in the same framework without deteriorating performance. Last, adding or removing demonstrations incurs low computational load, and thus, the robot’s skill library can be built incrementally. We first instantiate the proposed approach in a simulated task to validate these advantages. We then show that the advantages transfer to real hardware for a task where naive participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios, our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.
DOI BibTeX
Empirical Inference Conference Paper Invert to Learn to Invert Putzky, P., Welling, M. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 444-454, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Kernel Stein Tests for Multiple Model Comparison Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2240-2250, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Limitations of the empirical Fisher approximation for natural gradient descent Kunstner, F., Hennig, P., Balles, L. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Micro, Nano, and Molecular Systems Patent Method and apparatus for fabricating a component Melde, K., Fischer, P. (EP3034281B1), December 2019
The invention relates to a method of fabricating a component having a one-, two- or three-dimensional geometry, wherein the shape of the component is obtained by utilizing an acoustic field and the effect of acoustic forces and by fixating a shape that forms in the acoustic field. This may be achieved by accumulating a material, like e. g. discrete particles in the pressure nodes formed by the acoustic field. Furthermore, the invention relates to an apparatus for fabricating a component, including an acoustic source device for forming an acoustic field that gives rise to a shape, including shapes formed by a particle distribution by acoustic forces, and a fixation device for fixating a shape of the particle distribution. Applications of the invention are available in the fields of fabricating materials with arbitrary shapes, e.g. for rapid prototyping purposes, and the assembly of materials.
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Empirical Inference Talk Multivariate coupling estimation between continuous signals and point processes Safavi, S., Logothetis, N., Besserve, M. Neural Information Processing Systems 2019 - Workshop on Learning with Temporal Point Processes, December 2019 (Published) Talk video URL BibTeX
Empirical Inference Conference Paper On the Fairness of Disentangled Representations Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 14584-14597, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Optics and Sensing Laboratory Autonomous Motion Conference Paper On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset Gondal, M. W., Wüthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Perceiving the arrow of time in autoregressive motion Meding, K., Janzing, D., Schölkopf, B., Wichmann, F. A. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2303-2314, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Practical and Consistent Estimation of f-Divergences Rubenstein, P. K., Bousquet, O., Djolonga, J., Riquelme, C., Tolstikhin, I. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 4072-4082, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Ph.D. Thesis Real Time Probabilistic Models for Robot Trajectories Gomez-Gonzalez, S. Technical University Darmstadt, Germany, December 2019 (Published) BibTeX
Empirical Inference Ph.D. Thesis Robot Learning for Muscular Systems Büchler, D. Technical University Darmstadt, Germany, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Selecting causal brain features with a single conditional independence test per feature Mastakouri, A., Schölkopf, B., Janzing, D. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 12532-12543, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Stochastic Frank-Wolfe for Composite Convex Minimization Locatello, F., Yurtsever, A., Fercoq, O., Cevher, V. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 14246-14256, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Micro, Nano, and Molecular Systems Article Acoustic hologram enhanced phased arrays for ultrasonic particle manipulation Cox, L., Melde, K., Croxford, A., Fischer, P., Drinkwater, B. Phys. Rev. Applied, 12:064055, November 2019
The ability to shape ultrasound fields is important for particle manipulation, medical therapeutics and imaging applications. If the amplitude and/or phase is spatially varied across the wavefront then it is possible to project ‘acoustic images’. When attempting to form an arbitrary desired static sound field, acoustic holograms are superior to phased arrays due to their significantly higher phase fidelity. However, they lack the dynamic flexibility of phased arrays. Here, we demonstrate how to combine the high-fidelity advantages of acoustic holograms with the dynamic control of phased arrays in the ultrasonic frequency range. Holograms are used with a 64-element phased array, driven with continuous excitation. Moving the position of the projected hologram via phase delays which steer the output beam is demonstrated experimentally. This allows the creation of a much more tightly focused point than with the phased array alone, whilst still being reconfigurable. It also allows the complex movement at a water-air interface of a “phase surfer” along a phase track or the manipulation of a more arbitrarily shaped particle via amplitude traps. Furthermore, a particle manipulation device with two emitters and a single split hologram is demonstrated that allows the positioning of a “phase surfer” along a 1D axis. This paper opens the door for new applications with complex manipulation of ultrasound whilst minimising the complexity and cost of the apparatus.
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Haptic Intelligence Conference Paper Deep Neural Network Approach in Electrical Impedance Tomography-Based Real-Time Soft Tactile Sensor Park, H., Lee, H., Park, K., Mo, S., Kim, J. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (999)7447-7452, IEEE, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2019 (Published)
Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell's equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.
DOI BibTeX
Empirical Inference Conference Paper Improving Local Trajectory Optimisation using Probabilistic Movement Primitives Shyam, R. A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., Neumann, G. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2666-2671, IEEE, International Conference on Intelligent Robots and Systems 2019 (IROS) , November 2019 (Published) DOI BibTeX
Empirical Inference Conference Paper Experience Reuse with Probabilistic Movement Primitives Stark, S., Peters, J., Rueckert, E. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1210-1217, IEEE, November 2019 (Published) DOI BibTeX
Movement Generation and Control Conference Paper Learning to Explore in Motion and Interaction Tasks Bogdanovic, M., Righetti, L. Proceedings 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2686-2692, IEEE, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2019, ISSN: 2153-0866 (Published)
Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.
DOI BibTeX
Perceiving Systems Autonomous Vision Conference Paper Attacking Optical Flow Ranjan, A., Janai, J., Geiger, A., Black, M. J. In Proceedings International Conference on Computer Vision (ICCV), 2404-2413, IEEE, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), November 2019, ISSN: 2380-7504 (Published)
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.
Video Project Page Paper Supplementary Material DOI URL BibTeX
Haptic Intelligence Miscellaneous A Fabric-based Scalable Robotic Skin Mimicking Biological Tactile Hyperacuity Lee, H., Park, K., Kim, J., Kuchenbecker, K. J. Workshop paper (3 pages) presented at the IROS RoboTac Workshop on New Advances in Tactile Sensation, Perception, and Learning in Robotics: Emerging Materials and Technologies for Manipulation, Macao, China, November 2019, Co-Winner of the Award for Best Poster (Published)
Implementing a whole-body tactile sensor is becoming a critical topic in robotics since physical contacts can occur at any location of the robot. Fabricating such a large-scale system typically requires complex electrical wiring to achieve high spatial resolution. Interestingly, biological skins have tactile hyperacuity, which is enabled by overlapping the receptive fields. This study introduces a fabric-based tactile sensor inspired by this biological feature. The tactile sensor injects electrical current into a pair of electrodes and measures the corresponding electrical potentials formed around the current pathway, which can be considered as a receptive field. When two or more neighboring pairs of electrodes are sampled, sensitive regions overlap in a way similar to the biological system. For the experiments, a fabric-based tactile sensor with only 24 electrodes in an area of 200 mm × 200 mm is developed. The sensor can localize point contact with an error of 8.13 mm, while the sensor’s minimum two-point discrimination distance is nearly 35 mm. This performance is comparable to that of the stomach region of human skin. This sensing approach could greatly simplify whole-body tactile skin development in the future.
BibTeX
Perceiving Systems Conference Paper AirCap – Aerial Outdoor Motion Capture Ahmad, A., Price, E., Tallamraju, R., Saini, N., Lawless, G., Ludwig, R., Martinovic, I., Bülthoff, H. H., Black, M. J. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Workshop on Aerial Swarms, November 2019
This paper presents an overview of the Grassroots project Aerial Outdoor Motion Capture (AirCap) running at the Max Planck Institute for Intelligent Systems. AirCap's goal is to achieve markerless, unconstrained, human motion capture (mocap) in unknown and unstructured outdoor environments. To that end, we have developed an autonomous flying motion capture system using a team of aerial vehicles (MAVs) with only on-board, monocular RGB cameras. We have conducted several real robot experiments involving up to 3 aerial vehicles autonomously tracking and following a person in several challenging scenarios using our approach of active cooperative perception developed in AirCap. Using the images captured by these robots during the experiments, we have demonstrated a successful offline body pose and shape estimation with sufficiently high accuracy. Overall, we have demonstrated the first fully autonomous flying motion capture system involving multiple robots for outdoor scenarios.
Talk slides BibTeX
Empirical Inference Conference Paper Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics Celik, O., Abdulsamad, H., Peters, J. International Conference on Intelligent Robots and Systems (IROS), 6828-6833, IEEE, November 2019 (Published) DOI BibTeX
Perceiving Systems Article Decoding subcategories of human bodies from both body- and face-responsive cortical regions Foster, C., Zhao, M., Romero, J., Black, M. J., Mohler, B. J., Bartels, A., Bülthoff, I. NeuroImage, 202(15):116085, November 2019
Our visual system can easily categorize objects (e.g. faces vs. bodies) and further differentiate them into subcategories (e.g. male vs. female). This ability is particularly important for objects of social significance, such as human faces and bodies. While many studies have demonstrated category selectivity to faces and bodies in the brain, how subcategories of faces and bodies are represented remains unclear. Here, we investigated how the brain encodes two prominent subcategories shared by both faces and bodies, sex and weight, and whether neural responses to these subcategories rely on low-level visual, high-level visual or semantic similarity. We recorded brain activity with fMRI while participants viewed faces and bodies that varied in sex, weight, and image size. The results showed that the sex of bodies can be decoded from both body- and face-responsive brain areas, with the former exhibiting more consistent size-invariant decoding than the latter. Body weight could also be decoded in face-responsive areas and in distributed body-responsive areas, and this decoding was also invariant to image size. The weight of faces could be decoded from the fusiform body area (FBA), and weight could be decoded across face and body stimuli in the extrastriate body area (EBA) and a distributed body-responsive area. The sex of well-controlled faces (e.g. excluding hairstyles) could not be decoded from face- or body-responsive regions. These results demonstrate that both face- and body-responsive brain regions encode information that can distinguish the sex and weight of bodies. Moreover, the neural patterns corresponding to sex and weight were invariant to image size and could sometimes generalize across face and body stimuli, suggesting that such subcategorical information is encoded with a high-level visual or semantic code.
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Empirical Inference Conference Paper Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems Lutter, M., Listmann, K., Peters, J. International Conference on Intelligent Robots and Systems (IROS), 7718-7725, IEEE, November 2019 (Published) DOI BibTeX
Intelligent Control Systems Article Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees Baumann, D., Mager, F., Jacob, R., Thiele, L., Zimmerling, M., Trimpe, S. ACM Transactions on Cyber-Physical Systems, 4(2):18, November 2019 (Published) arXiv PDF DOI BibTeX
Empirical Inference Conference Paper Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories Urain, J., Peters, J. International Conference on Intelligent Robots and Systems (IROS), 1363-1369, IEEE, November 2019 (Published) DOI BibTeX
Haptic Intelligence Miscellaneous HuggieChest: An Inflatable Haptic Sensing Chest for a Hugging Robot Block, A. E., Kuchenbecker, K. J. Workshop paper (4 pages) presented at the IROS RoboTac Workshop on New Advances in Tactile Sensation, Perception, and Learning in Robotics: Emerging Materials and Technologies for Manipulation, Macao, China, November 2019 (Published)
During hugs, humans naturally provide and intuit subtle non-verbal cues that signify the desired strength and duration of an exchanged hug. Personal preferences for this close interaction may vary greatly between people; robots do not currently have the abilities to perceive or understand these preferences. This workshop paper discusses designing, building, and testing a novel inflatable chest that can simultaneously soften a robot and act as a tactile sensor to enable more natural and responsive hugging. Using PVC vinyl, two microphones, and two barometric pressure sensors, we created an inflatable two-chambered chest that forms the torso of a hugging robot. One chamber is located in the front of the robot, and the other chamber is in the back. While contacting HuggieChest in several ways common in hugs (start hug, rub, scratch, pat, squeeze, release), we recorded data from the two sensors in each chamber. The preliminary results suggest that the complementary haptic sensing channels allow the robot wearing the chest to detect coarse and fine contacts typically experienced during hugs, regardless of where the user contacts the robot. We also verified that we can detect contacts regardless of noise from the robot’s movement, as long as the HuggieChest is inflated within a certain pressure range.
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Empirical Inference Conference Paper Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction Trick, S., Koert, D., Peters, J., Rothkopf, C. A. International Conference on Intelligent Robots and Systems (IROS), 7009-7016, IEEE, November 2019 (Published) DOI BibTeX
Empirical Inference Conference Paper Receding Horizon Curiosity Schultheis, M., Belousov, B., Abdulsamad, H., Peters, J. Proceedings of the 3rd Annual Conference on Robot Learning (CoRL), 100:1278-1288, Proceedings of Machine Learning Research, (Editors: Leslie Pack Kaelbling and Danica Kragic and Komei Sugiura), PMLR, November 2019 (Published) URL BibTeX
Empirical Inference Conference Paper Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning Ewerton, M., Guilherme, M., Koert, D., Kolev, Z., Takahashi, M., Peters, J. International Conference on Intelligent Robots and Systems (IROS), 4294-4300, IEEE, November 2019 (Published) DOI BibTeX
Haptic Intelligence Miscellaneous Robust Visual Augmented Reality for Robot-Assisted Surgery Forte, M., Kuchenbecker, K. J. Extended abstract presented as a podium presentation at the IROS Workshop on Legacy Disruptors in Applied Telerobotics, Macao, China, November 2019 (Published) BibTeX
Physics for Inference and Optimization Article Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks Ruggeri, N., De Bacco, C. International Conference on Complex Networks and Their Applications, November 2019 (Published)
We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data collection is expensive, the network is too big for data storage capacityor only partial information is available. The sampling algorithm is theoretically grounded by results derivedfrom spectral approximation theory. We studied the problemon both synthetic and real data and tested theperformance comparing with traditional methods, such as random walk and uniform sampling. We show thatapproximations obtained from such methods are not always reliable and that our algorithm, while preservingcomputational scalability, improves performance under different error measures.
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