Header logo is


2017


Thumb xl screen shot 2017 06 14 at 2.38.22 pm
Scalable Pneumatic and Tendon Driven Robotic Joint Inspired by Jumping Spiders

Sproewitz, A., Göttler, C., Sinha, A., Caer, C., Öztekin, M. U., Petersen, K., Sitti, M.

In Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 64-70, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

dlg

Video link (url) DOI Project Page [BibTex]

2017


Video link (url) DOI Project Page [BibTex]


Thumb xl publications toc
Design and actuation of a magnetic millirobot under a constant unidirectional magnetic field

Erin, O., Giltinan, J., Tsai, L., Sitti, M.

In Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 3404-3410, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

Abstract
Magnetic untethered millirobots, which are actuated and controlled by remote magnetic fields, have been proposed for medical applications due to their ability to safely pass through tissues at long ranges. For example, magnetic resonance imaging (MRI) systems with a 3-7 T constant unidirectional magnetic field and 3D gradient coils have been used to actuate magnetic robots. Such magnetically constrained systems place limits on the degrees of freedom that can be actuated for untethered devices. This paper presents a design and actuation methodology for a magnetic millirobot that exhibits both position and orientation control in 2D under a magnetic field, dominated by a constant unidirectional magnetic field as found in MRI systems. Placing a spherical permanent magnet, which is free to rotate inside the millirobot and located away from the center of mass, allows the generation of net forces and torques with applied 3D magnetic field gradients. We model this system in a 3D planar case and experimentally demonstrate open-loop control of both position and orientation by the applied 2D field gradients. The actuation performance is characterized across the most important design variables, and we experimentally demonstrate that the proposed approach is feasible.

pi

DOI [BibTex]

DOI [BibTex]


no image
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Gu*, S., Holly*, E., Lillicrap, T., Levine, S.

Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017, *equal contribution (conference)

ei

Arxiv Project Page [BibTex]

Arxiv Project Page [BibTex]


Thumb xl publications toc
Magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy

Son, D., Dogan, M. D., Sitti, M.

In Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 1132-1139, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

Abstract
This paper presents a magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy (B-MASCE) in the upper gastrointestinal tract. A thin and hollow needle is attached to the capsule, which can penetrate deeply into tissues to obtain subsurface biopsy sample. The design utilizes a soft elastomer body as a compliant mechanism to guide the needle. An internal permanent magnet provides a means for both actuation and tracking. The capsule is designed to roll towards its target and then deploy the biopsy needle in a precise location selected as the target area. B-MASCE is controlled by multiple custom-designed electromagnets while its position and orientation are tracked by a magnetic sensor array. In in vitro trials, B-MASCE demonstrated rolling locomotion and biopsy of a swine tissue model positioned inside an anatomical human stomach model. It was confirmed after the experiment that a tissue sample was retained inside the needle.

pi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Proton 2: Increasing the Sensitivity and Portability of a Visuo-haptic Surface Interaction Recorder

Burka, A., Rajvanshi, A., Allen, S., Kuchenbecker, K. J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 439-445, Singapore, May 2017 (inproceedings)

Abstract
The Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short) is a new handheld visuo-haptic sensing system that records surface interactions. We previously demonstrated system calibration and a classification task using external motion tracking. This paper details improvements in surface classification performance and removal of the dependence on external motion tracking, necessary before embarking on our goal of gathering a vast surface interaction dataset. Two experiments were performed to refine data collection parameters. After adjusting the placement and filtering of the Proton's high-bandwidth accelerometers, we recorded interactions between two differently-sized steel tooling ball end-effectors (diameter 6.35 and 9.525 mm) and five surfaces. Using features based on normal force, tangential force, end-effector speed, and contact vibration, we trained multi-class SVMs to classify the surfaces using 50 ms chunks of data from each end-effector. Classification accuracies of 84.5% and 91.5% respectively were achieved on unseen test data, an improvement over prior results. In parallel, we pursued on-board motion tracking, using the Proton's camera and fiducial markers. Motion tracks from the external and onboard trackers agree within 2 mm and 0.01 rad RMS, and the accuracy decreases only slightly to 87.7% when using onboard tracking for the 9.525 mm end-effector. These experiments indicate that the Proton 2 is ready for portable data collection.

hi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Context-Driven Movement Primitive Adaptation

Wilbers, D., Lioutikov, R., Peters, J.

IEEE International Conference on Robotics and Automation (ICRA), pages: 3469-3475, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
A Learning-based Shared Control Architecture for Interactive Task Execution

Farraj, F. B., Osa, T., Pedemonte, N., Peters, J., Neumann, G., Giordano, P.

IEEE International Conference on Robotics and Automation (ICRA), pages: 329-335, IEEE, May 2017 (conference)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


no image
Frequency Peak Features for Low-Channel Classification in Motor Imagery Paradigms

Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 8th International IEEE/EMBS Conference on Neural Engineering (NER), pages: 321-324, May 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


no image
Empowered skills

Gabriel, A., Akrour, R., Peters, J., Neumann, G.

IEEE International Conference on Robotics and Automation (ICRA), pages: 6435-6441, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Thumb xl image  1
Human Shape Estimation using Statistical Body Models

Loper, M. M.

University of Tübingen, May 2017 (thesis)

Abstract
Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance. Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. We address this challenge with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates. Body state inference requires more than a body model; we therefore build obser- vation models whose output is compared with real observations. In this thesis, body state is estimated from three types of observations: 3D motion capture markers, depth and color images, and high-resolution 3D scans. In each case, a forward process is proposed which simulates observations. By comparing observations to the results of the forward process, state can be adjusted to minimize the difference between simulated and observed data. We use gradient-based methods because they are critical to the precise estimation of state with a large number of parameters. The contributions of this work include three parts. First, we propose a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers. Second, we present a concise approach to differentiating through the rendering process, with application to body shape estimation. And finally, we present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages.

ps

Official Version [BibTex]


no image
Layered direct policy search for learning hierarchical skills

End, F., Akrour, R., Peters, J., Neumann, G.

IEEE International Conference on Robotics and Automation (ICRA), pages: 6442-6448, IEEE, May 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

Gu, S., Lillicrap, T., Ghahramani, Z., Turner, R. E., Levine, S.

Proceedings International Conference on Learning Representations (ICLR), OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

PDF link (url) Project Page [BibTex]

PDF link (url) Project Page [BibTex]


no image
Categorical Reparametrization with Gumbel-Softmax

Jang, E., Gu, S., Poole, B.

Proceedings International Conference on Learning Representations 2017, OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
DeepCoder: Learning to Write Programs

Balog, M., Gaunt, A. L., Brockschmidt, M., Nowozin, S., Tarlow, D.

Proceedings International Conference on Learning Representations 2017, OpenReviews.net, International Conference on Learning Representations, April 2017 (conference)

ei

Arxiv link (url) Project Page [BibTex]

Arxiv link (url) Project Page [BibTex]


Thumb xl reliability icon
Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW), pages: 847-855, (Editors: Barrett, R., Cummings, R., Agichtein, E. and Gabrilovich, E. ), ACM, April 2017 (conference)

ei

Project DOI Project Page Project Page [BibTex]

Project DOI Project Page Project Page [BibTex]


no image
Local Group Invariant Representations via Orbit Embeddings

Raj, A., Kumar, A., Mroueh, Y., Fletcher, T., Schölkopf, B.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54, pages: 1225-1235, Proceedings of Machine Learning Research, (Editors: Aarti Singh and Jerry Zhu), April 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Thumb xl screen shot 2017 07 20 at 12.31.00 pm
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

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

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

pn

pdf link (url) Project Page [BibTex]

pdf link (url) Project Page [BibTex]


no image
Pre-Movement Contralateral EEG Low Beta Power Is Modulated with Motor Adaptation Learning

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages: 934-938, March 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


no image
Automatic detection of motion artifacts in MR images using CNNs

Meding, K., Loktyushin, A., Hirsch, M.

42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages: 811-815, March 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


Thumb xl eururol2017
Wireless micro-robots for endoscopic applications in urology

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

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

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

pf

link (url) DOI [BibTex]


no image
Catching heuristics are optimal control policies

Belousov, B., Neumann, G., Rothkopf, C., Peters, J.

Proceedings of the Thirteenth Karniel Computational Motor Control Workshop, March 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Thumb xl image toc
The use of clamping grips and friction pads by tree frogs for climbing curved surfaces

Endlein, T., Ji, A., Yuan, S., Hill, I., Wang, H., Barnes, W. J. P., Dai, Z., Sitti, M.

In Proc. R. Soc. B, 284(1849):20162867, Febuary 2017 (inproceedings)

Abstract
Most studies on the adhesive mechanisms of climbing animals have addressed attachment against flat surfaces, yet many animals can climb highly curved surfaces, like twigs and small branches. Here we investigated whether tree frogs use a clamping grip by recording the ground reaction forces on a cylindrical object with either a smooth or anti-adhesive, rough surface. Furthermore, we measured the contact area of fore and hindlimbs against differently sized transparent cylinders and the forces of individual pads and subarticular tubercles in restrained animals. Our study revealed that frogs use friction and normal forces of roughly a similar magnitude for holding on to cylindrical objects. When challenged with climbing a non-adhesive surface, the compressive forces between opposite legs nearly doubled, indicating a stronger clamping grip. In contrast to climbing flat surfaces, frogs increased the contact area on all limbs by engaging not just adhesive pads but also subarticular tubercles on curved surfaces. Our force measurements showed that tubercles can withstand larger shear stresses than pads. SEM images of tubercles revealed a similar structure to that of toe pads including the presence of nanopillars, though channels surrounding epithelial cells were less pronounced. The tubercles' smaller size, proximal location on the toes and shallow cells make them probably less prone to buckling and thus ideal for gripping curved surfaces.

pi

DOI [BibTex]

DOI [BibTex]


no image
DiSMEC – Distributed Sparse Machines for Extreme Multi-label Classification

Babbar, R., Schölkopf, B.

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM), pages: 721-729, Febuary 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Policy Search with High-Dimensional Context Variables

Tangkaratt, V., van Hoof, H., Parisi, S., Neumann, G., Peters, J., Sugiyama, M.

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pages: 2632-2638, (Editors: Satinder P. Singh and Shaul Markovitch), AAAI Press, Febuary 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

ei

PDF [BibTex]

PDF [BibTex]


no image
Iterative Feedback-basierte Korrekturstrategien beim Bewegungslernen von Mensch-Roboter-Dyaden

Ewerton, M., Kollegger, G., Maeda, G., Wiemeyer, J., Peters, J.

In DVS Sportmotorik 2017, 2017 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
BIMROB - Bidirectional Interaction between human and robot for the learning of movements - Robot trains human - Human trains robot

Kollegger, G., Wiemeyer, J., Ewerton, M., Peters, J.

In Inovation & Technologie im Sport - 23. Sportwissenschaftlicher Hochschultag der deutschen Vereinigung für Sportwissenschaft, pages: 179, (Editors: A. Schwirtz, F. Mess, Y. Demetriou & V. Senner ), Czwalina-Feldhaus, 2017 (inproceedings)

ei

[BibTex]

[BibTex]


no image
BIMROB – Bidirektionale Interaktion von Mensch und Roboter beim Bewegungslernen

Wiemeyer, J., Peters, J., Kollegger, G., Ewerton, M.

DVS Sportmotorik 2017, 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Comparison-based nearest neighbor search

Haghiri, S., Ghoshdastidar, D., von Luxburg, U.

In Artificial Intelligence and Statistics, Artificial Intelligence and Statistics (AISTATS), 2017 (inproceedings)

slt

Project Page [BibTex]

Project Page [BibTex]


Thumb xl screen shot 2018 02 08 at 12.58.55 pm
Linking Mechanics and Learning

Heim, S., Grimminger, F., Özge, D., Spröwitz, A.

In Proceedings of Dynamic Walking 2017, 2017 (inproceedings)

dlg

[BibTex]

[BibTex]


no image
Kernel functions based on triplet comparisons

Kleindessner, M., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS), 2017 (inproceedings)

slt

Project Page [BibTex]

Project Page [BibTex]


Thumb xl phd thesis teaser
Learning Inference Models for Computer Vision

Jampani, V.

MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

Abstract
Computer vision can be understood as the ability to perform 'inference' on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.

ps

pdf [BibTex]

pdf [BibTex]


Thumb xl 9780262036436
Mobile Microrobotics

Sitti, M.

Mobile Microrobotics, The MIT Press, Cambridge, MA, 2017 (book)

Abstract
Progress in micro- and nano-scale science and technology has created a demand for new microsystems for high-impact applications in healthcare, biotechnology, manufacturing, and mobile sensor networks. The new robotics field of microrobotics has emerged to extend our interactions and explorations to sub-millimeter scales. This is the first textbook on micron-scale mobile robotics, introducing the fundamentals of design, analysis, fabrication, and control, and drawing on case studies of existing approaches. The book covers the scaling laws that can be used to determine the dominant forces and effects at the micron scale; models forces acting on microrobots, including surface forces, friction, and viscous drag; and describes such possible microfabrication techniques as photo-lithography, bulk micromachining, and deep reactive ion etching. It presents on-board and remote sensing methods, noting that remote sensors are currently more feasible; studies possible on-board microactuators; discusses self-propulsion methods that use self-generated local gradients and fields or biological cells in liquid environments; and describes remote microrobot actuation methods for use in limited spaces such as inside the human body. It covers possible on-board powering methods, indispensable in future medical and other applications; locomotion methods for robots on surfaces, in liquids, in air, and on fluid-air interfaces; and the challenges of microrobot localization and control, in particular multi-robot control methods for magnetic microrobots. Finally, the book addresses current and future applications, including noninvasive medical diagnosis and treatment, environmental remediation, and scientific tools.

pi

Mobile Microrobotics By Metin Sitti - Chapter 1 (PDF) link (url) [BibTex]

Mobile Microrobotics By Metin Sitti - Chapter 1 (PDF) link (url) [BibTex]


no image
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

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

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

ei pn

DOI [BibTex]

DOI [BibTex]


Thumb xl publications toc
Planning spin-walking locomotion for automatic grasping of microobjects by an untethered magnetic microgripper

Dong, X., Sitti, M.

In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 6612-6618, 2017 (inproceedings)

Abstract
Most demonstrated mobile microrobot tasks so far have been achieved via pick-and-placing and dynamic trapping with teleoperation or simple path following algorithms. In our previous work, an untethered magnetic microgripper has been developed which has advanced functions, such as gripping objects. Both teleoperated manipulation in 2D and 3D have been demonstrated. However, it is challenging to control the magnetic microgripper to carry out manipulation tasks, because the grasping of objects so far in the literature relies heavily on teleoperation, which takes several minutes with even a skilled human expert. Here, we propose a new spin-walking locomotion and an automated 2D grasping motion planner for the microgripper, which enables time-efficient automatic grasping of microobjects that has not been achieved yet for untethered microrobots. In its locomotion, the microgripper repeatedly rotates about two principal axes to regulate its pose and move precisely on a surface. The motion planner could plan different motion primitives for grasping and compensate the uncertainties in the motion by learning the uncertainties and planning accordingly. We experimentally demonstrated that, using the proposed method, the microgripper could align to the target pose with error less than 0.1 body length and grip the objects within 40 seconds. Our method could significantly improve the time efficiency of micro-scale manipulation and have potential applications in microassembly and biomedical engineering.

pi

DOI Project Page [BibTex]

DOI Project Page [BibTex]


no image
Die Bedeutung der Beobachtungsperspektive beim Bewegungslernen von Mensch-Roboter-Dyaden

Kollegger, G., Reinhardt, N., Ewerton, M., Peters, J., Wiemeyer, J.

DVS Sportmotorik 2017, 2017 (conference)

link (url) [BibTex]

link (url) [BibTex]


no image
Development and Evaluation of a Portable BCI System for Remote Data Acquisition

Emde, T.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

ei

[BibTex]

[BibTex]


no image
Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis

Fomina, T.

Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

ei

[BibTex]

[BibTex]


Thumb xl muvs
Towards Accurate Marker-less Human Shape and Pose Estimation over Time

Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P. V., Romero, J., Akhter, I., Black, M. J.

In International Conference on 3D Vision (3DV), pages: 421-430, 2017 (inproceedings)

Abstract
Existing markerless motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, limiting their application scenarios. Here we present a fully automatic method that, given multiview videos, estimates 3D human pose and body shape. We take the recently proposed SMPLify method [12] as the base method and extend it in several ways. First we fit a 3D human body model to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours, further improving accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging monocular sequences of dancing from YouTube.

ps

Code pdf DOI Project Page [BibTex]


Thumb xl screen shot 2018 02 08 at 12.58.55 pm
Is Growing Good for Learning?

Heim, S., Spröwitz, A.

Proceedings of the 8th International Symposium on Adaptive Motion of Animals and Machines AMAM2017, 2017 (conference)

dlg

[BibTex]

[BibTex]


Thumb xl paraview preview
Design of a visualization scheme for functional connectivity data of Human Brain

Bramlage, L.

Hochschule Osnabrück - University of Applied Sciences, 2017 (thesis)

sf

Bramlage_BSc_2017.pdf [BibTex]


no image
Causal models for decision making via integrative inference

Geiger, P.

University of Stuttgart, Germany, 2017 (phdthesis)

ei

[BibTex]

[BibTex]


Thumb xl coverhand wilson
Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Tzionas, D.

University of Bonn, 2017 (phdthesis)

Abstract
Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object's shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow.

ps

Thesis link (url) Project Page [BibTex]


Thumb xl screen shot 2018 02 08 at 1.12.35 pm
Evaluation of the passive dynamics of compliant legs with inertia

Györfi, B.

University of Applied Science Pforzheim, Germany, 2017 (mastersthesis)

dlg

[BibTex]

[BibTex]


no image
Two-sample tests for large random graphs using network statistics

Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.

In Conference on Computational Learning Theory (COLT), Conference on Computational Learning Theory (COLT), 2017 (inproceedings)

slt

Project Page [BibTex]

Project Page [BibTex]


no image
Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

Sücker, K.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

ei

[BibTex]

[BibTex]


no image
Understanding FORC using synthetic micro-structured systems with variable coupling- and coercivefield distributions

Groß, Felix

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

mms

[BibTex]


no image
Pattern Generation for Walking on Slippery Terrains

Khadiv, M., Moosavian, S. A. A., Herzog, A., Righetti, L.

In 2017 5th International Conference on Robotics and Mechatronics (ICROM), Iran, August 2017 (inproceedings)

Abstract
In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this for- mulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.

mg

link (url) [BibTex]

link (url) [BibTex]