Publications | Max Planck Institute for Intelligent Systems

am ei Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J. Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 690-695, Humanoids, November 2015 (inproceedings)

Evaluation of Interactive Object Recognition with Tactile Sensing

Hoelscher, J., Peters, J., Hermans, T.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 310-317, Humanoids, November 2015 (inproceedings)

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am ei Hoelscher, J., Peters, J., Hermans, T. Evaluation of Interactive Object Recognition with Tactile Sensing In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 310-317, Humanoids, November 2015 (inproceedings)

Method and device for recovering a digital image from a sequence of observed digital images

Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B., Schuler, C.

European Patent, No. 11767924.1, November 2015 (patent)

ei Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B., Schuler, C. Method and device for recovering a digital image from a sequence of observed digital images European Patent, No. 11767924.1, November 2015 (patent)

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, pages: 80-87, Humanoids, November 2015 (inproceedings)

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am ei Koc, O., Maeda, G., Neumann, G., Peters, J. Optimizing Robot Striking Movement Primitives with Iterative Learning Control In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 80-87, Humanoids, November 2015 (inproceedings)

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, pages: 616-622, Humanoids, November 2015 (inproceedings)

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am ei Leischnig, S., Luettgen, S., Kroemer, O., Peters, J. A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 616-622, Humanoids, November 2015 (inproceedings)

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, pages: 997-1002, Humanoids, November 2015 (inproceedings)

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am ei Fritsche, L., Unverzagt, F., Peters, J., Calandra, R. First-Person Tele-Operation of a Humanoid Robot In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 997-1002, Humanoids, November 2015 (inproceedings)

Probabilistic Segmentation Applied to an Assembly Task

Lioutikov, R., Neumann, G., Maeda, G., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

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am ei Lioutikov, R., Neumann, G., Maeda, G., Peters, J. Probabilistic Segmentation Applied to an Assembly Task In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

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), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract

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.

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am ei ics pn Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S. Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

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, pages: 209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (inproceedings)

ei Tolstikhin, I., Zhivotovskiy, N., Blanchard, G. Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning In Proceedings of the 26th International Conference on Algorithmic Learning Theory, 9355, pages: 209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (inproceedings)

Towards Probabilistic Volumetric Reconstruction using Ray Potentials

(Best Paper Award)

Ulusoy, A. O., Geiger, A., Black, M. J.

In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

Abstract

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.

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code YouTube pdf suppmat DOI Project Page [BibTex]

avg ps Ulusoy, A. O., Geiger, A., Black, M. J. Towards Probabilistic Volumetric Reconstruction using Ray Potentials In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

code YouTube pdf suppmat DOI Project Page [BibTex]

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, pages: 32-39, September 2015 (inproceedings)

Abstract

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.

pi Colmenares, D., Kania, R., Zhang, W., Sitti, M. Compliant wing design for a flapping wing micro air vehicle In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, pages: 32-39, September 2015 (inproceedings)

Methods of forming dry adhesive structures

Sitti, M., Murphy, M., Aksak, B.

September 2015, US Patent 9,120,953 (patent)

Abstract

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.

pi Sitti, M., Murphy, M., Aksak, B. Methods of forming dry adhesive structures September 2015, US Patent 9,120,953 (patent)

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, pages: 5065-5072, IROS, September 2015 (inproceedings)

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am ei Veiga, F., van Hoof, H., Peters, J., Hermans, T. Stabilizing Novel Objects by Learning to Predict Tactile Slip In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)

Millimeter-scale magnetic swimmers using elastomeric undulations

Zhang, J., Diller, E.

In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1706-1711, September 2015 (inproceedings)

Abstract

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.

pi Zhang, J., Diller, E. Millimeter-scale magnetic swimmers using elastomeric undulations In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1706-1711, September 2015 (inproceedings)

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, pages: 2860-2866, IROS, September 2015 (inproceedings)

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am ei Paraschos, A., Rueckert, E., Peters, J., Neumann, G. Model-Free Probabilistic Movement Primitives for Physical Interaction In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)

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, pages: 852-857, IROS, September 2015 (inproceedings)

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am ei Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H. Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)

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, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

Abstract

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.

ps Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J. Perception of Strength and Power of Realistic Male Characters In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

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, pages: 449-455, IROS, September 2015 (inproceedings)

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am ei Manschitz, S., Kober, J., Gienger, M., Peters, J. Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)

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, pages: 6428-6434, IROS, September 2015 (inproceedings)

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am ei Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J. Reinforcement Learning vs Human Programming in Tetherball Robot Games In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)

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, pages: 456-463, IROS, September 2015 (inproceedings)

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am ei Ewerton, M., Maeda, G., Peters, J., Neumann, G. Learning Motor Skills from Partially Observed Movements Executed at Different Speeds In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 456-463, IROS, September 2015 (inproceedings)

3D-printed Soft Microrobot for Swimming in Biological Fluids

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

In Conf. Proc. IEEE Eng. Med. Biol. Soc., pages: 4922-4925, August 2015 (inproceedings)

Abstract

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.

pf Qiu, T., Palagi, S., Fischer, P. 3D-printed Soft Microrobot for Swimming in Biological Fluids In Conf. Proc. IEEE Eng. Med. Biol. Soc., pages: 4922-4925, August 2015 (inproceedings)

Ibarra Chaoul, A., Grosse-Wentrup, M.

Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power?

Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

ei Ibarra Chaoul, A., Grosse-Wentrup, M. Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power? Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

Micro-fiber arrays with tip coating and transfer method for preparing same

Sitti, M., Washburn, N. R., Glass, P. S., Chung, H.

July 2015, US Patent 9,079,215 (patent)

Abstract

Present invention describes a patterned and coated micro- and nano-scale fibers elastomeric material for enhanced adhesion in wet or dry environments. A multi-step fabrication process including optical lithography, micromolding, polymer synthesis, dipping, stamping, and photopolymerization is described to produce uniform arrays of micron-scale fibers with mushroom-shaped tips coated with a thin layer of an intrinsically adhesive synthetic polymer, such as lightly crosslinked p(DMA-co-MEA).

pi Sitti, M., Washburn, N. R., Glass, P. S., Chung, H. Micro-fiber arrays with tip coating and transfer method for preparing same July 2015, US Patent 9,079,215 (patent)

Retrospective motion correction of magnitude-input MR images

Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.

First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)

ei Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B. Retrospective motion correction of magnitude-input MR images First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)

arXiv-preprint pdf DOI Project Page [BibTex]

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

Jampani, V., Nowozin, S., Loper, M., Gehler, P. V.

In Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

Abstract

Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favored efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way in our informed sampler and in careful experiments demonstrate it on challenging models which contain renderer programs as their components. The informed sampler, using simple discriminative proposals based on existing computer vision technology achieves dramatic improvements in inference. Our approach enables a new richness in generative models that was out of reach with existing inference technology.

ps Jampani, V., Nowozin, S., Loper, M., Gehler, P. V. The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models In Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

arXiv-preprint pdf DOI Project Page [BibTex]

Direct Loss Minimization Inverse Optimal Control

Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.

In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

Abstract

Inverse Optimal Control (IOC) has strongly impacted the systems engineering process, enabling automated planner tuning through straightforward and intuitive demonstration. The most successful and established applications, though, have been in lower dimensional problems such as navigation planning where exact optimal planning or control is feasible. In higher dimensional systems, such as humanoid robots, research has made substantial progress toward generalizing the ideas to model free or locally optimal settings, but these systems are complicated to the point where demonstration itself can be difficult. Typically, real-world applications are restricted to at best noisy or even partial or incomplete demonstrations that prove cumbersome in existing frameworks. This work derives a very flexible method of IOC based on a form of Structured Prediction known as Direct Loss Minimization. The resulting algorithm is essentially Policy Search on a reward function that rewards similarity to demonstrated behavior (using Covariance Matrix Adaptation (CMA) in our experiments). Our framework blurs the distinction between IOC, other forms of Imitation Learning, and Reinforcement Learning, enabling us to derive simple, versatile, and practical algorithms that blend imitation and reinforcement signals into a unified framework. Our experiments analyze various aspects of its performance and demonstrate its efficacy on conveying preferences for motion shaping and combined reach and grasp quality optimization.

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am ics Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S. Direct Loss Minimization Inverse Optimal Control In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

LMI-Based Synthesis for Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceedings of the American Control Conference, July 2015 (inproceedings)

Abstract

This paper presents an LMI-based synthesis procedure for distributed event-based state estimation. Multiple agents observe and control a dynamic process by sporadically exchanging data over a broadcast network according to an event-based protocol. In previous work [1], the synthesis of event-based state estimators is based on a centralized design. In that case three different types of communication are required: event-based communication of measurements, periodic reset of all estimates to their joint average, and communication of inputs. The proposed synthesis problem eliminates the communication of inputs as well as the periodic resets (under favorable circumstances) by accounting explicitly for the distributed structure of the control system.

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am ics Muehlebach, M., Trimpe, S. LMI-Based Synthesis for Distributed Event-Based State Estimation In Proceedings of the American Control Conference, July 2015 (inproceedings)

Guaranteed H2 Performance in Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

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am ics Muehlebach, M., Trimpe, S. Guaranteed H2 Performance in Distributed Event-Based State Estimation In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose

Zuffi, S., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

Abstract

We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D as well as deform to capture pose-dependent shape variations. Pairwise potentials define a “stitching cost” for pulling the limbs apart, giving rise to the stitched puppet model (SPM). Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives the SPM the realism of recent 3D body models with the computational advantages of part-based models. We apply the SPM to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge (http://faust.is.tue.mpg.de/), where ours is the first method to successfully align all 3D meshes. The second involves estimating pose and shape from crude visual hull representations of complex body movements.

ps Zuffi, S., Black, M. J. The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

On the Choice of the Event Trigger in Event-based Estimation

Trimpe, S., Campi, M.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

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am ics Trimpe, S., Campi, M. On the Choice of the Event Trigger in Event-based Estimation In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

Displets: Resolving Stereo Ambiguities using Object Knowledge

Güney, F., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

Abstract

Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation.

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pdf abstract suppmat [BibTex]

avg ps Güney, F., Geiger, A. Displets: Resolving Stereo Ambiguities using Object Knowledge In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

pdf abstract suppmat [BibTex]

Object Scene Flow for Autonomous Vehicles

Menze, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

Abstract

This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which can't be handled by existing methods.

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pdf abstract suppmat DOI [BibTex]

avg ps Menze, M., Geiger, A. Object Scene Flow for Autonomous Vehicles In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 (inproceedings)

pdf abstract suppmat DOI [BibTex]

Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction

Akhter, I., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

Abstract

The estimation of 3D human pose from 2D joint locations is central to many vision problems involving the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collected a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. The dataset and the prior will be made publicly available. Second, we define a general parameterization of body pose and a new, multistage, method to estimate 3D pose from 2D joint locations that uses an over-complete dictionary of human poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results on manual annotations on real images and automatic part-based detections on the Leeds sports pose dataset.

ps Akhter, I., Black, M. J. Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers

Wulff, J., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 120-130, June 2015 (inproceedings)

Abstract

We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed from four Hollywood movies. Optical flow fields are then compactly approximated as a weighted sum of the basis flow fields. Our new PCA-Flow algorithm robustly estimates these weights from sparse feature matches. The method runs in under 300ms/frame on the MPI-Sintel dataset using a single CPU and is more accurate and significantly faster than popular methods such as LDOF and Classic+NL. The results, however, are too smooth for some applications. Consequently, we develop a novel sparse layered flow method in which each layer is represented by PCA-flow. Unlike existing layered methods, estimation is fast because it uses only sparse matches. We combine information from different layers into a dense flow field using an image-aware MRF. The resulting PCA-Layers method runs in 3.6s/frame, is significantly more accurate than PCA-flow and achieves state-of-the-art performance in occluded regions on MPI-Sintel.

pdf Extended Abstract Supplemental Material Poster Code Project Page Project Page [BibTex]

ps Wulff, J., Black, M. J. Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 120-130, June 2015 (inproceedings)

pdf Extended Abstract Supplemental Material Poster Code Project Page Project Page [BibTex]

Permutohedral Lattice CNNs

Kiefel, M., Jampani, V., Gehler, P. V.

In ICLR Workshop Track, ICLR, May 2015 (inproceedings)

Abstract

This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

ei ps

pdf link (url) [BibTex]

ei ps Kiefel, M., Jampani, V., Gehler, P. V. Permutohedral Lattice CNNs In ICLR Workshop Track, ICLR, May 2015 (inproceedings)

pdf link (url) [BibTex]

Fiberbot: A miniature crawling robot using a directional fibrillar pad

Han, Y., Marvi, H., Sitti, M.

In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages: 3122-3127, May 2015 (inproceedings)

Abstract

Vibration-driven locomotion has been widely used for crawling robot studies. Such robots usually have a vibration motor as the actuator and a fibrillar structure for providing directional friction on the substrate. However, there has not been any studies about the effect of fiber structure on robot crawling performance. In this paper, we develop Fiberbot, a custom made mini vibration robot, for studying the effect of fiber angle on robot velocity, steering, and climbing performance. It is known that the friction force with and against fibers depends on the fiber angle. Thus, we first present a new fabrication method for making millimeter scale fibers at a wide range of angles. We then show that using 30° angle fibers that have the highest friction anisotropy (ratio of backward to forward friction force) among the other fibers we fabricated in this study, Fiberbot speed on glass increases to 13.8±0.4 cm/s (compared to ν = 0.6±0.1 cm/s using vertical fibers). We also demonstrate that the locomotion direction of Fiberbot depends on the tilting direction of fibers and we can steer the robot by rotating the fiber pad. Fiberbot could also climb on glass at inclinations of up to 10° when equipped with fibers of high friction anisotropy. We show that adding a rigid tail to the robot it can climb on glass at 25° inclines. Moreover, the robot is able to crawl on rough surfaces such as wood (ν = 10.0±0.2 cm/s using 30° fiber pad). Fiberbot, a low-cost vibration robot equipped with a custom-designed fiber pad with steering and climbing capabilities could be used for studies on collective behavior on a wide range of topographies as well as search and exploratory missions.

pi Han, Y., Marvi, H., Sitti, M. Fiberbot: A miniature crawling robot using a directional fibrillar pad In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages: 3122-3127, May 2015 (inproceedings)

Platform design and tethered flight of a motor-driven flapping-wing system

Hines, L., Colmenares, D., Sitti, M.

In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages: 5838-5845, May 2015 (inproceedings)

Abstract

In this work, we examine two design modifications to a tethered motor-driven flapping-wing system. Previously, we had demonstrated a simple mechanism utilizing a linear transmission for resonant operation and direct drive of the wing flapping angle for control. The initial two-wing system had a weight of 2.7 grams and a maximum lift-to-weight ratio of 1.4. While capable of vertical takeoff, in open-loop flight it demonstrated instability and pitch oscillations at the wing flapping frequency, leading to flight times of only a few wing strokes. Here the effect of vertical wing offset as well as an alternative multi-wing layout is investigated and experimentally tested with newly constructed prototypes. With only a change in vertical wing offset, stable open-loop flight of the two-wing flapping system is shown to be theoretically possible, but difficult to achieve with our current design and operating parameters. Both of the new two and four-wing systems, however, prove capable of flying to the end of the tether, with the four-wing system prototype eliminating disruptive wing beat oscillations.

pi Hines, L., Colmenares, D., Sitti, M. Platform design and tethered flight of a motor-driven flapping-wing system In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages: 5838-5845, May 2015 (inproceedings)

online pdf supplementary link (url) [BibTex]

Leveraging Big Data for Grasp Planning

Kappler, D., Bohg, B., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard Îµ-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the Îµ-metric and therefore lead to a better classification performance.

PDF data DOI Project Page [BibTex]

am Kappler, D., Bohg, B., Schaal, S. Leveraging Big Data for Grasp Planning In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

PDF data DOI Project Page [BibTex]

Consensus Message Passing for Layered Graphical Models

Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J.

In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, Eighteenth International Conference on Artificial Intelligence and Statistics, May 2015 (inproceedings)

Abstract

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.

ps Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J. Consensus Message Passing for Layered Graphical Models In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, Eighteenth International Conference on Artificial Intelligence and Statistics, May 2015 (inproceedings)

online pdf supplementary link (url) [BibTex]

Event-based Estimation and Control for Remote Robot Operation with Reduced Communication

Trimpe, S., Buchli, J.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster response scenarios such as the DARPA Robotics Challenge.

am ics

am ics Trimpe, S., Buchli, J. Event-based Estimation and Control for Remote Robot Operation with Reduced Communication In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

arXiv Video Bayesian Filtering Framework Bayesian Object Tracking DOI Project Page [BibTex]

The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems

Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form. For nonparametric filters, such as the Particle Filter, the converse holds. Such methods are able to approximate any posterior, but the computational requirements scale exponentially with the number of dimensions of the state space. In this paper, we present the Coordinate Particle Filter which alleviates this problem. We propose to compute the particle weights recursively, dimension by dimension. This allows us to explore one dimension at a time, and resample after each dimension if necessary. Experimental results on simulated as well as real data con- firm that the proposed method has a substantial performance advantage over the Particle Filter in high-dimensional systems where not all dimensions are highly correlated. We demonstrate the benefits of the proposed method for the problem of multi-object and robotic manipulator tracking.

am Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S. The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

arXiv Video Bayesian Filtering Framework Bayesian Object Tracking DOI Project Page [BibTex]

Dry adhesives and methods for making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2015, US Patent App. 14/625,162 (patent)

Abstract

Dry adhesives and methods for forming dry adhesives. A method of forming a dry adhesive structure on a substrate, comprises: forming a template backing layer of energy sensitive material on the substrate; forming a template layer of energy sensitive material on the template backing layer; exposing the template layer to a predetermined pattern of energy; removing a portion of the template layer related to the predetermined pattern of energy, and leaving a template structure formed from energy sensitive material and connected to the substrate via the template backing layer.

pi Sitti, M., Murphy, M., Aksak, B. Dry adhesives and methods for making dry adhesives March 2015, US Patent App. 14/625,162 (patent)

Understanding the Geometry of Workspace Obstacles in Motion Optimization

Ratliff, N., Toussaint, M., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, March 2015 (inproceedings)

am Ratliff, N., Toussaint, M., Schaal, S. Understanding the Geometry of Workspace Obstacles in Motion Optimization In Proceedings of the IEEE International Conference on Robotics and Automation, March 2015 (inproceedings)

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

Efficient Facade Segmentation using Auto-Context

Jampani, V., Gadde, R., Gehler, P. V.

In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, WACV,, January 2015 (inproceedings)

Abstract

In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using auto-context features and learned using the stacked generalization technique. We find that this, albeit standard, technique performs better, or equals, all previous published empirical results on all available facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test time inference.

ps Jampani, V., Gadde, R., Gehler, P. V. Efficient Facade Segmentation using Auto-Context In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, WACV,, January 2015 (inproceedings)

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

Adaptive information-theoretic bounded rational decision-making with parametric priors

Grau-Moya, J, Braun, DA

pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)

Abstract

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.

ei Grau-Moya, J, Braun, DA Adaptive information-theoretic bounded rational decision-making with parametric priors pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)