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Empirical Inference Conference Paper Adaptive information-theoretic bounded rational decision-making with parametric priors Grau-Moya, J., Braun, D. 1-4, NIPS 2015 Workshop on Bounded Optimality and Rational Metareasoning, December 2015
Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.
BibTeX
Perceiving Systems Conference Paper 3D Object Reconstruction from Hand-Object Interactions Tzionas, D., Gall, J. In International Conference on Computer Vision (ICCV), 729-737, International Conference on Computer Vision (ICCV), December 2015
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.
pdf Project's Website Video Spotlight Extended Abstract YouTube DOI BibTeX
Perceiving Systems Conference Paper Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences Bogo, F., Black, M. J., Loper, M., Romero, J. In International Conference on Computer Vision (ICCV), 2300-2308, December 2015
We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.
Video pdf BibTeX
Empirical Inference Article Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories Peng, Z., Braun, D. Frontiers in Psychology, 6(1879):1-13, December 2015
In a previous study we have shown that human motion trajectories can be characterized by translating continuous trajectories into symbol sequences with well-defined complexity measures. Here we test the hypothesis that the motion complexity individuals generate in their movements might be correlated to the degree of creativity assigned by a human observer to the visualized motion trajectories. We asked participants to generate 55 novel hand movement patterns in virtual reality, where each pattern had to be repeated 10 times in a row to ensure reproducibility. This allowed us to estimate a probability distribution over trajectories for each pattern. We assessed motion complexity not only by the previously proposed complexity measures on symbolic sequences, but we also propose two novel complexity measures that can be directly applied to the distributions over trajectories based on the frameworks of Gaussian Processes and Probabilistic Movement Primitives. In contrast to previous studies, these new methods allow computing complexities of individual motion patterns from very few sample trajectories. We compared the different complexity measures to how a group of independent jurors rank ordered the recorded motion trajectories according to their personal creativity judgment. We found three entropic complexity measures that correlate significantly with human creativity judgment and discuss differences between the measures. We also test whether these complexity measures correlate with individual creativity in divergent thinking tasks, but do not find any consistent correlation. Our results suggest that entropic complexity measures of hand motion may reveal domain-specific individual differences in kinesthetic creativity.
DOI BibTeX
Micro, Nano, and Molecular Systems Article Enzymatically active biomimetic micropropellers for the penetration of mucin gels Walker (Schamel), D., Käsdorf, B. T., Jeong, H. H., Lieleg, O., Fischer, P. Science Advances, 1(11):e1500501, December 2015 (Published)
In the body, mucus provides an important defense mechanism by limiting the penetration of pathogens. It is therefore also a major obstacle for the efficient delivery of particle-based drug carriers. The acidic stomach lining in particular is difficult to overcome because mucin glycoproteins form viscoelastic gels under acidic conditions. The bacterium Helicobacter pylori has developed a strategy to overcome the mucus barrier by producing the enzyme urease, which locally raises the pH and consequently liquefies the mucus. This allows the bacteria to swim through mucus and to reach the epithelial surface. We present an artificial system of reactive magnetic micropropellers that mimic this strategy to move through gastric mucin gels by making use of surface-immobilized urease. The results demonstrate the validity of this biomimetic approach to penetrate biological gels, and show that externally propelled microstructures can actively and reversibly manipulate the physical state of their surroundings, suggesting that such particles could potentially penetrate native mucus.
DOI URL BibTeX
Perceiving Systems Autonomous Vision Conference Paper Exploiting Object Similarity in 3D Reconstruction Zhou, C., Güney, F., Wang, Y., Geiger, A. In International Conference on Computer Vision (ICCV), December 2015 (Published)
Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavor. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by locating objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.
pdf suppmat BibTeX
Perceiving Systems Autonomous Vision Conference Paper FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation Lenz, P., Geiger, A., Urtasun, R. In International Conference on Computer Vision (ICCV), International Conference on Computer Vision (ICCV), December 2015 (Published)
One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.
pdf suppmat video project BibTeX
Perceiving Systems Conference Paper Intrinsic Depth: Improving Depth Transfer with Intrinsic Images Kong, N., Black, M. J. In IEEE International Conference on Computer Vision (ICCV), 3514-3522, International Conference on Computer Vision (ICCV), December 2015
We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation. Our approach synergistically integrates the estimation of multiple intrinsic images including depth, albedo, shading, optical flow, and surface contours. We build upon an example-based framework for depth estimation that uses label transfer from a database of RGB and depth pairs. We combine this with a method that extracts consistent albedo and shading from video. In contrast to raw RGB values, albedo and shading provide a richer, more physical, foundation for depth transfer. Additionally we train a new contour detector to predict surface boundaries from albedo, shading, and pixel values and use this to improve the estimation of depth boundaries. We also integrate sparse structure from motion with our method to improve the metric accuracy of the estimated depth maps. We evaluate our Intrinsic Depth method quantitatively by estimating depth from videos in the NYU RGB-D and SUN3D datasets. We find that combining the estimation of multiple intrinsic images improves depth estimation relative to the baseline method.
pdf suppmat YouTube official video poster BibTeX
Perceiving Systems Software Workshop Article Scalable Robust Principal Component Analysis using Grassmann Averages Hauberg, S., Feragen, A., Enficiaud, R., Black, M. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.
preprint pdf from publisher supplemental BibTeX
Empirical Inference Article What is epistemic value in free energy models of learning and acting? A bounded rationality perspective Ortega, P., Braun, D. Cognitive Neuroscience, 6(4):215-216, December 2015
Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.
DOI BibTeX
Physical Intelligence Book Chapter Untethered Magnetic Micromanipulation Diller, E., Sitti, M. In Micro-and Nanomanipulation Tools, 13, 10, Wiley-VCH Verlag GmbH & Co. KGaA, November 2015
This chapter discusses the methods and state of the art in microscale manipulation in remote environments using untethered microrobotic devices. It focuses on manipulation at the size scale of tens to hundreds of microns, where small size leads to a dominance of microscale physical effects and challenges in fabrication and actuation. To motivate the challenges of operating at this size scale, the chapter includes coverage of the physical forces relevant to microrobot motion and manipulation below the millimeter-size scale. It then introduces the actuation methods commonly used in untethered manipulation schemes, with particular focus on magnetic actuation due to its wide use in the field. The chapter divides these manipulation techniques into two types: contact manipulation, which relies on direct pushing or grasping of objects for motion, and noncontact manipulation, which relies indirectly on induced fluid flow from the microrobot motion to move objects without any direct contact.
DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions Leischnig, S., Luettgen, S., Kroemer, O., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 616-622, Humanoids, November 2015 (Published) DOI BibTeX
Autonomous Motion Intelligent Control Systems Technical Report Distributed Event-based State Estimation Trimpe, S. Max Planck Institute for Intelligent Systems, November 2015
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.
arXiv BibTeX
Empirical Inference Article Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (Published) DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper Evaluation of Interactive Object Recognition with Tactile Sensing Hoelscher, J., Peters, J., Hermans, T. In 15th IEEE-RAS International Conference on Humanoid Robots, 310-317, Humanoids, November 2015 (Published) DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper First-Person Tele-Operation of a Humanoid Robot Fritsche, L., Unverzagt, F., Peters, J., Calandra, R. In 15th IEEE-RAS International Conference on Humanoid Robots, 997-1002, Humanoids, November 2015 (Published) DOI URL BibTeX
Empirical Inference Autonomous Motion Conference Paper Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 690-695, Humanoids, November 2015 (Published) DOI URL BibTeX
Empirical Inference Autonomous Motion Conference Paper Optimizing Robot Striking Movement Primitives with Iterative Learning Control Koc, O., Maeda, G., Neumann, G., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 80-87, Humanoids, November 2015 (Published) DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper Probabilistic Segmentation Applied to an Assembly Task Lioutikov, R., Neumann, G., Maeda, G., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 533-540, Humanoids, November 2015 (Published) DOI BibTeX
Empirical Inference Article Quantifying changes in climate variability and extremes: Pitfalls and their overcoming Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D. Geophysical Research Letters, 42(22):9990-9998, November 2015 DOI BibTeX
Empirical Inference Ph.D. Thesis easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies Grimm, D. Eberhard Karls Universität Tübingen, November 2015 BibTeX
Empirical Inference Ph.D. Thesis Causal Discovery Beyond Conditional Independences Sgouritsa, E. Eberhard Karls Universität Tübingen, Germany, October 2015 (Published) URL BibTeX
Autonomous Motion Empirical Inference Probabilistic Numerics Intelligent Control Systems Conference Paper Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S. Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (Published)
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.
PDF DOI BibTeX
Empirical Inference Article Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle Genewein, T., Leibfried, F., Grau-Moya, J., Braun, D. Frontiers in Robotics and AI, 2(27):1-24, October 2015
Abstraction and hierarchical information-processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such a flexibility in artificial systems is challenging, even with more and more computational power. Here we investigate the hypothesis that abstraction and hierarchical information-processing might in fact be the consequence of limitations in information-processing power. In particular, we study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions. We show how the formation of abstractions and decision-making hierarchies depends on information-processing costs. We illustrate the theoretical ideas with example simulations and conclude by formalizing a mathematically unifying optimization principle that could potentially be extended to more complex systems.
DOI BibTeX
Empirical Inference Poster Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (Published) URL BibTeX
Autonomous Motion Intelligent Control Systems Master Thesis Gaussian Process Optimization for Self-Tuning Control Marco, A. Polytechnic University of Catalonia (BarcelonaTech), October 2015 PDF BibTeX
Empirical Inference Article Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M. Journal of Vision, 15(14):1-17, October 2015 (Published) DOI BibTeX
Empirical Inference Conference Paper Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning Tolstikhin, I., Zhivotovskiy, N., Blanchard, G. In Proceedings of the 26th International Conference on Algorithmic Learning Theory, 9355:209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (Published) DOI BibTeX
Perceiving Systems Proceedings Proceedings of the 37th German Conference on Pattern Recognition Gall, J., Gehler, P., Leibe, B. Springer, German Conference on Pattern Recognition, October 2015 GCPR conference website BibTeX
Perceiving Systems Autonomous Vision Conference Paper Towards Probabilistic Volumetric Reconstruction using Ray Potentials Ulusoy, A. O., Geiger, A., Black, M. J. In 3D Vision (3DV), 2015 3rd International Conference on, 10-18, Lyon, October 2015
This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.
code YouTube pdf suppmat DOI BibTeX
Perceiving Systems Article SMPL: A Skinned Multi-Person Linear Model Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J. ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.
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Empirical Inference Talk Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism Besserve, M. 53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (Published) BibTeX
Empirical Inference Ph.D. Thesis From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding Muandet, K. University of Tübingen, Germany, University of Tübingen, Germany, September 2015 BibTeX
Empirical Inference Ph.D. Thesis Machine Learning Approaches to Image Deconvolution Schuler, C. University of Tübingen, Germany, University of Tübingen, Germany, September 2015 BibTeX
Physical Intelligence Conference Paper Compliant wing design for a flapping wing micro air vehicle Colmenares, D., Kania, R., Zhang, W., Sitti, M. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, 32-39, September 2015
In this work, we examine several wing designs for a motor-driven, flapping-wing micro air vehicle capable of liftoff. The full system consists of two wings independently driven by geared pager motors that include a spring in parallel with the output shaft. The linear transmission allows for resonant operation, while control is achieved by direct drive of the wing angle. Wings used in previous work were chosen to be fully rigid for simplicity of modeling and fabrication. However, biological wings are highly flexible and other micro air vehicles have successfully utilized flexible wing structures for specialized tasks. The goal of our study is to determine if wing flexibility can be generally used to increase wing performance. Two approaches to lift improvement using flexible wings are explored, resonance of the wing cantilever structure and dynamic wing twisting. We design and test several wings that are compared using different figures of merit. A twisted design improved lift per power by 73.6% and maximum lift production by 53.2% compared to the original rigid design. Wing twist is then modeled in order to propose optimal wing twist profiles that can maximize either wing efficiency or lift production.
DOI BibTeX
Physical Intelligence Patent Methods of forming dry adhesive structures Sitti, M., Murphy, M., Aksak, B. September 2015, US Patent 9,120,953
Methods of forming dry adhesives including a method of making a dry adhesive including applying a liquid polymer to the second end of the stem, molding the liquid polymer on the stem in a mold, wherein the mold includes a recess having a cross-sectional area that is less than a cross-sectional area of the second end of the stem, curing the liquid polymer in the mold to form a tip at the second end of the stem, wherein the tip includes a second layer stem; corresponding to the recess in the mold, and removing the tip from the mold after the liquid polymer cures.
BibTeX
Autonomous Motion Intelligent Control Systems Master Thesis Adaptive and Learning Concepts in Hydraulic Force Control Doerr, A. University of Stuttgart, September 2015 BibTeX
Empirical Inference Autonomous Motion Conference Paper Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 852-857, IROS, September 2015 (Published) DOI URL BibTeX
Empirical Inference Autonomous Motion Conference Paper Learning Motor Skills from Partially Observed Movements Executed at Different Speeds Ewerton, M., Maeda, G., Peters, J., Neumann, G. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 456-463, IROS, September 2015 (Published) DOI URL BibTeX
Physical Intelligence Conference Paper Millimeter-scale magnetic swimmers using elastomeric undulations Zhang, J., Diller, E. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1706-1711, September 2015
This paper presents a new soft-bodied millimeterscale swimmer actuated by rotating uniform magnetic fields. The proposed swimmer moves through internal undulatory deformations, resulting from a magnetization profile programmed into its body. To understand the motion of the swimmer, a mathematical model is developed to describe the general relationship between the deflection of a flexible strip and its magnetization profile. As a special case, the situation of the swimmer on the water surface is analyzed and predictions made by the model are experimentally verified. Experimental results show the controllability of the proposed swimmer under a computer vision-based closed-loop controller. The swimmers have nominal dimensions of 1.5×4.9×0.06 mm and a top speed of 50 mm/s (10 body lengths per second). Waypoint following and multiagent control are demonstrated for swimmers constrained at the air-water interface and underwater swimming is also shown, suggesting the promising potential of this type of swimmer in biomedical and microfluidic applications.
DOI URL BibTeX
Empirical Inference Autonomous Motion Conference Paper Model-Free Probabilistic Movement Primitives for Physical Interaction Paraschos, A., Rueckert, E., Peters, J., Neumann, G. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 2860-2866, IROS, September 2015 (Published) DOI URL BibTeX
Perceiving Systems Conference Paper Perception of Strength and Power of Realistic Male Characters Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J. In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, 7-14, ACM, New York, NY, September 2015
We investigated the influence of body shape and pose on the perception of physical strength and social power for male virtual characters. In the first experiment, participants judged the physical strength of varying body shapes, derived from a statistical 3D body model. Based on these ratings, we determined three body shapes (weak, average, and strong) and animated them with a set of power poses for the second experiment. Participants rated how strong or powerful they perceived virtual characters of varying body shapes that were displayed in different poses. Our results show that perception of physical strength was mainly driven by the shape of the body. However, the social attribute of power was influenced by an interaction between pose and shape. Specifically, the effect of pose on power ratings was greater for weak body shapes. These results demonstrate that a character with a weak shape can be perceived as more powerful when in a high-power pose.
PDF DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives Manschitz, S., Kober, J., Gienger, M., Peters, J. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 449-455, IROS, September 2015 (Published) DOI URL BibTeX
Empirical Inference Autonomous Motion Conference Paper Reinforcement Learning vs Human Programming in Tetherball Robot Games Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 6428-6434, IROS, September 2015 (Published) DOI URL BibTeX
Empirical Inference Article Semi-Supervised Interpolation in an Anticausal Learning Scenario Janzing, D., Schölkopf, B. Journal of Machine Learning Research, 16:1923-1948, September 2015 (Published) URL BibTeX
Empirical Inference Article Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S. PLOS Biology, 13(9):e1002257, September 2015 (Published) DOI BibTeX
Empirical Inference Autonomous Motion Conference Paper Stabilizing Novel Objects by Learning to Predict Tactile Slip Veiga, F., van Hoof, H., Peters, J., Hermans, T. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, 5065-5072, IROS, September 2015 (Published) DOI URL BibTeX
Micro, Nano, and Molecular Systems Article The EChemPen: A Guiding Hand To Learn Electrochemical Surface Modifications Valetaud, M., Loget, G., Roche, J., Hueken, N., Fattah, Z., Badets, V., Fontaine, O., Zigah, D. J. of Chem. Ed., 92(10):1700-1704, September 2015 (Published)
The Electrochemical Pen (EChemPen) was developed as an attractive tool for learning electrochemistry. The fabrication, principle, and operation of the EChemPen are simple and can be easily performed by students in practical classes. It is based on a regular fountain pen principle, where the electrolytic solution is dispensed at a tip to locally modify a conductive surface by triggering a localized electrochemical reaction. Three simple model reactions were chosen to demonstrate the versatility of the EChemPen for teaching various electrochemical processes. We describe first the reversible writing/erasing of metal letters, then the electrodeposition of a black conducting polymer "ink", and finally the colorful writings that can be generated by titanium anodization and that can be controlled by the applied potential. These entertaining and didactic experiments are adapted for teaching undergraduate students that start to study electrochemistry by means of surface modification reactions.
DOI BibTeX
Physical Intelligence Article Experimental investigation of optimal adhesion of mushroomlike elastomer microfibrillar adhesives Marvi, H., Song, S., Sitti, M. Langmuir, 31(37):10119-10124, American Chemical Society, August 2015
Optimal fiber designs for the maximal pull-off force have been indispensable for increasing the attachment performance of recently introduced gecko-inspired reversible micro/nanofibrillar adhesives. There are several theoretical studies on such optimal designs; however, due to the lack of three-dimensional (3D) fabrication techniques that can fabricate such optimal designs in 3D, there have not been many experimental investigations on this challenge. In this study, we benefitted from recent advances in two-photon lithography techniques to fabricate mushroomlike polyurethane elastomer fibers with different aspect ratios of tip to stalk diameter (β) and tip wedge angles (θ) to investigate the effect of these two parameters on the pull-off force. We found similar trends to those predicted theoretically. We found that β has an impact on the slope of the force-displacement curve while both β and θ play a role in the stress distribution and crack propagation. We found that these effects are coupled and the optimal set of parameters also depends on the fiber material. This is the first experimental verification of such optimal designs proposed for mushroomlike microfibers. This experimental approach could be used to evaluate a wide range of complex microstructured adhesive designs suggested in the literature and optimize them.
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Micro, Nano, and Molecular Systems Conference Paper 3D-printed Soft Microrobot for Swimming in Biological Fluids Qiu, T., Palagi, S., Fischer, P. In Conf. Proc. IEEE Eng. Med. Biol. Soc., 4922-4925, Piscataway, NJ, USA, August 2015 (Published)
Microscopic artificial swimmers hold the potential to enable novel non-invasive medical procedures. In order to ease their translation towards real biomedical applications, simpler designs as well as cheaper yet more reliable materials and fabrication processes should be adopted, provided that the functionality of the microrobots can be kept. A simple single-hinge design could already enable microswimming in non-Newtonian fluids, which most bodily fluids are. Here, we address the fabrication of such single-hinge microrobots with a 3D-printed soft material. Firstly, a finite element model is developed to investigate the deformability of the 3D-printed microstructure under typical values of the actuating magnetic fields. Then the microstructures are fabricated by direct 3D-printing of a soft material and their swimming performances are evaluated. The speeds achieved with the 3D-printed microrobots are comparable to those obtained in previous work with complex fabrication procedures, thus showing great promise for 3D-printed microrobots to be operated in biological fluids.
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