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2017


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Generating Descriptions with Grounded and Co-Referenced People

Rohrbach, A., Rohrbach, M., Tang, S., Oh, S. J., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4196-4206, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

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PDF DOI Project Page [BibTex]

2017


PDF DOI Project Page [BibTex]


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Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

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

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense 3D reconstruction of the scene as well as the existence and precise 3D pose of the objects in it. Our approach is able to recover fine details not captured in the input shapes while defaulting to the input models in occluded regions where image evidence is weak. Due to its probabilistic nature, the approach is able to cope with the approximate geometry of the 3D models as well as input shapes that are not present in the scene. We evaluate the approach quantitatively on several challenging indoor and outdoor datasets.

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YouTube pdf suppmat Project Page [BibTex]

YouTube pdf suppmat Project Page [BibTex]


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Deep representation learning for human motion prediction and classification

Bütepage, J., Black, M., Kragic, D., Kjellström, H.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

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arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


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Unite the People: Closing the Loop Between 3D and 2D Human Representations

Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., Gehler, P. V.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits “in-the-wild”. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.

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arXiv project/code/data Project Page [BibTex]

arXiv project/code/data Project Page [BibTex]


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Dynamic analysis on hexapedal water-running robot with compliant joints

Kim, H., Liu, Y., Jeong, K., Sitti, M., Seo, T.

In 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pages: 250-251, June 2017 (inproceedings)

Abstract
The dynamic analysis has been considered as one of the important design methods to design robots. In this research, we derive dynamic equation of hexapedal water-running robot to design compliant joints. The compliant joints that connect three bodies will be used to improve mobility and stability of water-running motion's pitch behavior. We considered all of parts as rigid body including links of six Klann mechanisms and three main frames. And then, we derived dynamic equation by using the Lagrangian method with external force of the water. We are expecting that the dynamic analysis is going to be used to design parts of the water running robot.

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DOI [BibTex]

DOI [BibTex]


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Soiled adhesive pads shear clean by slipping: a robust self-cleaning mechanism in climbing beetles

Amador, G., Endlein, T., Sitti, M.

Journal of The Royal Society Interface, 14(131):20170134, The Royal Society, June 2017 (article)

Abstract
Animals using adhesive pads to climb smooth surfaces face the problem of keeping their pads clean and functional. Here, a self-cleaning mechanism is proposed whereby soiled feet would slip on the surface due to a lack of adhesion but shed particles in return. Our study offers an in situ quantification of self-cleaning performance in fibrillar adhesives, using the dock beetle as a model organism. After beetles soiled their pads by stepping into patches of spherical beads, we found that their gait was significantly affected. Specifically, soiled pads slipped 10 times further than clean pads, with more particles deposited for longer slips. Like previous studies, we found that particle size affected cleaning performance. Large (45 μm) beads were removed most effectively, followed by medium (10 μm) and small (1 μm). Consistent with our results from climbing beetles, force measurements on freshly severed legs revealed larger detachment forces of medium particles from adhesive pads compared to a flat surface, possibly due to interlocking between fibres. By contrast, dock leaves showed an overall larger affinity to the beads and thus reduced the need for cleaning. Self-cleaning through slippage provides a mechanism robust to particle size and may inspire solutions for artificial adhesives.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Yield prediction in parallel homogeneous assembly

Ipparthi, D., Winslow, A., Sitti, M., Dorigo, M., Mastrangeli, M.

Soft Matter, 13, pages: 7595-7608, The Royal Society of Chemistry, June 2017 (article)

Abstract
We investigate the parallel assembly of two-dimensional{,} geometrically-closed modular target structures out of homogeneous sets of macroscopic components of varying anisotropy. The yield predicted by a chemical reaction network (CRN)-based model is quantitatively shown to reproduce experimental results over a large set of conditions. Scaling laws for parallel assembling systems are then derived from the model. By extending the validity of the CRN-based modelling{,} this work prompts analysis and solutions to the incompatible substructure problem.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Three‐dimensional patterning in biomedicine: Importance and applications in neuropharmacology

Ajay, V. S., Tanmay, G., Madu, B., Byung‐Wook, P., Thomas, E., Metin, S.

Journal of Biomedical Materials Research Part B: Applied Biomaterials, 106(3):1369-1382, June 2017 (article)

Abstract
Abstract Nature manufactures biological systems in three dimensions with precisely controlled spatiotemporal profiles on hierarchical length and time scales. In this article, we review 3D patterning of biological systems on synthetic platforms for neuropharmacological applications. We briefly describe 3D versus 2D chemical and topographical patterning methods and their limitations. Subsequently, an overview of introducing a third dimension in neuropharmacological research with delineation of chemical and topographical roles is presented. Finally, toward the end of this article, an explanation of how 3D patterning has played a pivotal role in relevant fields of neuropharmacology to understand neurophysiology during development, normal health, and disease conditions is described. The future prospects of organs‐on‐a‐‐like devices to mimic patterned blood–brain barrier in the context of neurotherapeutic discovery and development for the prioritization of lead candidates, membrane potential, and toxicity testing are also described. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1369–1382, 2018.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Tail-Assisted Mobility and Stability Enhancement in Yaw/Pitch Motions of a Water-Running Robot

Kim, H., Sitti, M., Seo, T.

IEEE/ASME Transactions on Mechatronics, 22(3):1207–1217, IEEE, June 2017 (article)

Abstract
Water-running robots have been developed inspired by a basilisk lizard, which demonstrates highly agile, stable, and energy-efficient locomotion on water surfaces. Current water-running robots are not as stable and agile as their biological counterparts. This study shows how the stability of a water-running robot in the pitch direction can be improved by using an active tail to enable increased propulsion. The mobility of the robot is also increased. To generate force in the pitch and yaw directions, a two-degrees-of-freedom tail is implemented with two circular plates to provide drag. We developed two types of dynamic models for pitch and yaw behavior, and the results are recursively calculated by considering the correlation between the models. The relationship between pitch motion and propulsion was analyzed by simulations. The steering behavior of the robot is also validated while considering the pitch behavior. Experiments were conducted to verify the simulation results.

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DOI [BibTex]

DOI [BibTex]


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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.

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DOI [BibTex]

DOI [BibTex]


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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.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Propulsion and Chemotaxis in Bacteria-Driven Microswimmers

Zhuang, J., Park, B., Sitti, M.

Advanced Science, 4(9):1700109, May 2017 (article)

Abstract
Despite the large body of experimental work recently on biohybrid microsystems, few studies have focused on theoretical modeling of such systems, which is essential to understand their underlying functioning mechanisms and hence design them optimally for a given application task. Therefore, this study focuses on developing a mathematical model to describe the 3D motion and chemotaxis of a type of widely studied biohybrid microswimmer, where spherical microbeads are driven by multiple attached bacteria. The model is developed based on the biophysical observations of the experimental system and is validated by comparing the model simulation with experimental 3D swimming trajectories and other motility characteristics, including mean squared displacement, speed, diffusivity, and turn angle. The chemotaxis modeling results of the microswimmers also agree well with the experiments, where a collective chemotactic behavior among multiple bacteria is observed. The simulation result implies that such collective chemotaxis behavior is due to a synchronized signaling pathway across the bacteria attached to the same microswimmer. Furthermore, the dependencies of the motility and chemotaxis of the microswimmers on certain system parameters, such as the chemoattractant concentration gradient, swimmer body size, and number of attached bacteria, toward an optimized design of such biohybrid system are studied. The optimized microswimmers would be used in targeted cargo, e.g., drug, imaging agent, gene, and RNA, transport and delivery inside the stagnant or low-velocity fluids of the human body as one of their potential biomedical applications.

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DOI Project Page [BibTex]


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Dynamic and programmable self-assembly of micro-rafts at the air-water interface

Wang, W., Giltinan, J., Zakharchenko, S., Sitti, M.

Science Advances, 3(5):e1602522, American Association for the Advancement of Science, May 2017 (article)

Abstract
Dynamic self-assembled material systems constantly consume energy to maintain their spatiotemporal structures and functions. Programmable self-assembly translates information from individual parts to the collective whole. Combining dynamic and programmable self-assembly in a single platform opens up the possibilities to investigate both types of self-assembly simultaneously and to explore their synergy. This task is challenging because of the difficulty in finding suitable interactions that are both dissipative and programmable. We present a dynamic and programmable self-assembling material system consisting of spinning at the air-water interface circular magnetic micro-rafts of radius 50 μm and with cosinusoidal edge-height profiles. The cosinusoidal edge-height profiles not only create a net dissipative capillary repulsion that is sustained by continuous torque input but also enable directional assembly of micro-rafts. We uncover the layered arrangement of micro-rafts in the patterns formed by dynamic self-assembly and offer mechanistic insights through a physical model and geometric analysis. Furthermore, we demonstrate programmable self-assembly and show that a 4-fold rotational symmetry encoded in individual micro-rafts translates into 90° bending angles and square-based tiling in the assembled structures of micro-rafts. We anticipate that our dynamic and programmable material system will serve as a model system for studying nonequilibrium dynamics and statistical mechanics in the future

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DOI [BibTex]

DOI [BibTex]


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Presentation of functional groups on self-assembled supramolecular peptide nanofibers mimicking glycosaminoglycans for directed mesenchymal stem cell differentiation

Yasa, O., Uysal, O., Ekiz, M. S., Guler, M. O., Tekinay, A. B.

J. Mater. Chem. B, 5, pages: 4890-4900, The Royal Society of Chemistry, May 2017 (article)

Abstract
Organizational complexity and functional diversity of the extracellular matrix regulate cellular behaviors. The extracellular matrix is composed of various proteins in the form of proteoglycans{,} glycoproteins{,} and nanofibers whose types and combinations change depending on the tissue type. Proteoglycans{,} which are proteins that are covalently attached to glycosaminoglycans{,} contribute to the complexity of the microenvironment of the cells. The sulfation degree of the glycosaminoglycans is an important and distinct feature at specific developmental stages and tissue types. Peptide amphiphile nanofibers can mimic natural glycosaminoglycans and/or proteoglycans{,} and they form a synthetic nanofibrous microenvironment where cells can proliferate and differentiate towards different lineages. In this study{,} peptide nanofibers were used to provide varying degrees of sulfonation mimicking the natural glycosaminoglycans by forming a microenvironment for the survival and differentiation of stem cells. The effects of glucose{,} carboxylate{,} and sulfonate groups on the peptide nanofibers were investigated by considering the changes in the differentiation profiles of rat mesenchymal stem cells in the absence of any specific differentiation inducers in the culture medium. The results showed that a higher sulfonate-to-glucose ratio is associated with adipogenic differentiation and a higher carboxylate-to-glucose ratio is associated with osteochondrogenic differentiation of the rat mesenchymal stem cells. Overall{,} these results demonstrate that supramolecular peptide nanosystems can be used to understand the fine-tunings of the extracellular matrix such as sulfation profile on specific cell types.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl drotlef et al 2017 advanced materials
Bioinspired Composite Microfibers for Skin Adhesion and Signal Amplification of Wearable Sensors

Drotlef, D., Amjadi, M., Yunusa, M., Sitti, M.

Advanced Materials, 29(28):1701353, May 2017, Back Cover (article)

Abstract
A facile approach is proposed for superior conformation and adhesion of wearable sensors to dry and wet skin. Bioinspired skin-adhesive films are composed of elastomeric microfibers decorated with conformal and mushroom-shaped vinylsiloxane tips. Strong skin adhesion is achieved by crosslinking the viscous vinylsiloxane tips directly on the skin surface. Furthermore, composite microfibrillar adhesive films possess a high adhesion strength of 18 kPa due to the excellent shape adaptation of the vinylsiloxane tips to the multiscale roughness of the skin. As a utility of the skin-adhesive films in wearable-device applications, they are integrated with wearable strain sensors for respiratory and heart-rate monitoring. The signal-to-noise ratio of the strain sensor is significantly improved to 59.7 because of the considerable signal amplification of microfibrillar skin-adhesive films.

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DOI [BibTex]


Thumb xl mostaghaci et al 2017 advanced science
Bioadhesive Bacterial Microswimmers for Targeted Drug Delivery in the Urinary and Gastrointestinal Tracts

Mostaghaci, B., Yasa, O., Zhuang, J., Sitti, M.

Advanced Science, 4(6):1700058, May 2017 (article)

Abstract
Bacteria-driven biohybrid microswimmers (bacteriabots), which integrate motile bacterial cells and functional synthetic cargo parts (e.g., microparticles encapsulating drug), are recently studied for targeted drug delivery. However, adhesion of such bacteriabots to the tissues on the site of a disease (which can increase the drug delivery efficiency) is not studied yet. Here, this paper proposes an approach to attach bacteriabots to certain types of epithelial cells (expressing mannose on the membrane), based on the affinity between lectin molecules on the tip of bacterial type I pili and mannose molecules on the epithelial cells. It is shown that the bacteria can anchor their cargo particles to mannose-functionalized surfaces and mannose-expressing cells (ATCC HTB-9) using the lectin–mannose bond. The attachment mechanism is confirmed by comparing the adhesion of bacteriabots fabricated from bacterial strains with or without type I pili to mannose-covered surfaces and cells. The proposed bioadhesive motile system can be further improved by expressing more specific adhesion moieties on the membrane of the bacteria.

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DOI Project Page [BibTex]


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Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

Turan, M., Abdullah, A., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.06196, May 2017 (article)

Abstract
Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six degreeof-freedom (DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the doctors correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, our idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases vignetting, reflection distortions, noise, sudden camera movements and lack of distinguishable features.

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DOI Project Page [BibTex]


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Controllable load sharing for soft adhesive interfaces on three-dimensional surfaces

Song, S., Drotlef, D., Majidi, C., Sitti, M.

Proceedings of the National Academy of Sciences, 114(22):E4344–E4353, National Acad Sciences, May 2017 (article)

Abstract
For adhering to three-dimensional (3D) surfaces or objects, current adhesion systems are limited by a fundamental trade-off between 3D surface conformability and high adhesion strength. This limitation arises from the need for a soft, mechanically compliant interface, which enables conformability to nonflat and irregularly shaped surfaces but significantly reduces the interfacial fracture strength. In this work, we overcome this trade-off with an adhesion-based soft-gripping system that exhibits enhanced fracture strength without sacrificing conformability to nonplanar 3D surfaces. Composed of a gecko-inspired elastomeric microfibrillar adhesive membrane supported by a pressure-controlled deformable gripper body, the proposed soft-gripping system controls the bonding strength by changing its internal pressure and exploiting the mechanics of interfacial equal load sharing. The soft adhesion system can use up to ∼26% of the maximum adhesion of the fibrillar membrane, which is 14× higher than the adhering membrane without load sharing. Our proposed load-sharing method suggests a paradigm for soft adhesion-based gripping and transfer-printing systems that achieves area scaling similar to that of a natural gecko footpad.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.05444, May 2017 (article)

Abstract
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI tract and to diagnose various diseases and pathologies. Since the development of this technology, medical device companies and many groups have made significant progress to turn such passive capsule endoscopes into robotic active capsule endoscopes to achieve almost all functions of current active flexible endoscopes. However, the use of robotic capsule endoscopy still has some challenges. One such challenge is the precise localization of such active devices in 3D world, which is essential for a precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of the explored inner organ could assist the doctors to make more intuitive and correct diagnosis. In this paper, we propose to our knowledge for the first time in literature a visual simultaneous localization and mapping (SLAM) method specifically developed for endoscopic capsule robots. The proposed RGB-Depth SLAM method is capable of capturing comprehensive dense globally consistent surfel-based maps of the inner organs explored by an endoscopic capsule robot in real time. This is achieved by using dense frame-to-model camera tracking and windowed surfelbased fusion coupled with frequent model refinement through non-rigid surface deformations.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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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.

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Official Version [BibTex]


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Hydrophobic pinning with copper nanowhiskers leads to bactericidal properties

Singh, A. V., Baylan, S., Park, B., Richter, G., Sitti, M.

PloS One, 12(4):e0175428, Public Library of Science, April 2017 (article)

Abstract
The considerable morbidity associated with hospitalized patients and clinics in developed countries due to biofilm formation on biomedical implants and surgical instruments is a heavy economic burden. An alternative to chemically treated surfaces for bactericidal activity started emerging from micro/nanoscale topographical cues in the last decade. Here, we demonstrate a putative antibacterial surface using copper nanowhiskers deposited by molecular beam epitaxy. Furthermore, the control of biological response is based on hydrophobic pinning of water droplets in the Wenzel regime, causing mechanical injury and cell death. Scanning electron microscopy images revealed the details of the surface morphology and non-contact mode laser scanning of the surface revealed the microtopography-associated quantitative parameters. Introducing the bacterial culture over nanowhiskers produces mechanical injury to cells, leading to a reduction in cell density over time due to local pinning of culture medium to whisker surfaces. Extended culture to 72 hours to observe biofilm formation revealed biofilm inhibition with scattered microcolonies and significantly reduced biovolume on nanowhiskers. Therefore, surfaces patterned with copper nanowhiskers can serve as potential antibiofilm surfaces. The topography-based antibacterial surfaces introduce a novel prospect in developing mechanoresponsive nanobiomaterials to reduce the risk of medical device biofilm-associated infections, contrary to chemical leaching of copper as a traditional bactericidal agent.

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link (url) [BibTex]

link (url) [BibTex]


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Biohybrid microtube swimmers driven by single captured bacteria

Stanton, M. M., Park, B., Miguel-López, A., Ma, X., Sitti, M., Sánchez, S.

Small, 13(19), March 2017 (article)

Abstract
Bacteria biohybrids employ the motility and power of swimming bacteria to carry and maneuver microscale particles. They have the potential to perform microdrug and cargo delivery in vivo, but have been limited by poor design, reduced swimming capabilities, and impeded functionality. To address these challenge, motile Escherichia coli are captured inside electropolymerized microtubes, exhibiting the first report of a bacteria microswimmer that does not utilize a spherical particle chassis. Single bacterium becomes partially trapped within the tube and becomes a bioengine to push the microtube though biological media. Microtubes are modified with “smart” material properties for motion control, including a bacteria-attractant polydopamine inner layer, addition of magnetic components for external guidance, and a biochemical kill trigger to cease bacterium swimming on demand. Swimming dynamics of the bacteria biohybrid are quantified by comparing “length of protrusion” of bacteria from the microtubes with respect to changes in angular autocorrelation and swimmer mean squared displacement. The multifunctional microtubular swimmers present a new generation of biocompatible micromotors toward future microbiorobots and minimally invasive medical applications.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Sticky Solution Provides Grip for the First Robotic Pollinator

Amador, G. J., Hu, D. L.

Chem, 2(2):162 - 164, Febuary 2017 (article)

Abstract
Bees, move over. A lily has been pollinated by a remote-controlled flying robot. The robot is hairy, just like a real bee, and sticks to pollen by virtue of an ionic liquid gel, whose fabrication is discussed by Svetlana Chechetka et al. in this issue of Chem.

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link (url) DOI [BibTex]


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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.

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DOI [BibTex]

DOI [BibTex]


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Rubbing Against Blood Clots Using Helical Robots: Modeling and In Vitro Experimental Validation

Khalil, I. S., Tabak, A. F., Sadek, K., Mahdy, D., Hamdi, N., Sitti, M.

IEEE Robotics and Automation Letters, 2(2):927-934, IEEE, January 2017 (article)

Abstract
The risk of side effects from thrombolytic agents can be minimized by using smaller doses, assisted by mechanical rubbing against blood clots using helical robots. Quantifying this observation, we study the influence of rubbing against clots on their removal rate in vitro. First, we present a hydrodynamic model of the helical robot based on the resistive-force theory to investigate the rubbing behavior of the clots using robot driven by two rotating dipole fields. Second, we experimentally evaluate the influence of the rubbing on the removal rate of the blood clots. Not only do we find that the removal rate of mechanical rubbing (-0.56 ± 0.27 mm3 /min) is approximately three times greater than the dissolution rate of chemical lysis using streptokinase (-0.17 ± 0.032 mm3/min), but we also show that this removal rate can be controlled via the rubbing speed of the robot.

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DOI [BibTex]

DOI [BibTex]


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Nanoscale topographical control of capillary assembly of nanoparticles

Flauraud, V., Mastrangeli, M., Bernasconi, G., Butet, J., Alexander, D., Shahrabi, E., Martin, O., Brugger, J.

Scientific Reports, Nature Nanotechnology, 12, pages: 73-80, January 2017 (article)

Abstract
Predetermined and selective placement of nanoparticles onto large-area substrates with nanometre-scale precision is essential to harness the unique properties of nanoparticle assemblies, in particular for functional optical and electro-optical nanodevices. Unfortunately, such high spatial organization is currently beyond the reach of top-down nanofabrication techniques alone. Here, we demonstrate that topographic features comprising lithographed funnelled traps and auxiliary sidewalls on a solid substrate can deterministically direct the capillary assembly of Au nanorods to attain simultaneous control of position, orientation and interparticle distance at the nanometre level. We report up to 100% assembly yield over centimetre-scale substrates. We achieve this by optimizing the three sequential stages of capillary nanoparticle assembly: insertion of nanorods into the traps, resilience against the receding suspension front and drying of the residual solvent. Finally, using electron energy-loss spectroscopy we characterize the spectral response and near-field properties of spatially programmable Au nanorod dimers, highlighting the opportunities for precise tunability of the plasmonic modes in larger assemblies.

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DOI [BibTex]

DOI [BibTex]


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Multiaxial Polarity Determines Individual Cellular and Nuclear Chirality

Raymond, M. J., Ray, P., Kaur, G., Fredericks, M., Singh, A. V., Wan, L. Q.

Cellular and Molecular Bioengineering, 10(1):63-74, 2017 (article)

Abstract
Intrinsic cell chirality has been implicated in the left--right (LR) asymmetry of embryonic development. Impaired cell chirality could lead to severe birth defects in laterality. Previously, we detected cell chirality with an in vitro micropatterning system. Here, we demonstrate for the first time that chirality can be quantified as the coordination of multiaxial polarization of individual cells and nuclei. Using an object labeling, connected component based method, we characterized cell chirality based on cell and nuclear shape polarization and nuclear positioning of each cell in multicellular patterns of epithelial cells. We found that the cells adopted a LR bias the boundaries by positioning the sharp end towards the leading edge and leaving the nucleus at the rear. This behavior is consistent with the directional migration observed previously on the boundary of micropatterns. Although the nucleus is chirally aligned, it is not strongly biased towards or away from the boundary. As the result of the rear positioning of nuclei, the nuclear positioning has an opposite chirality to that of cell alignment. Overall, our results have revealed deep insights of chiral morphogenesis as the coordination of multiaxial polarization at the cellular and subcellular levels.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Early Stopping Without a Validation Set

Mahsereci, M., Balles, L., Lassner, C., Hennig, P.

arXiv preprint arXiv:1703.09580, 2017 (article)

Abstract
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel early stopping criterion which is based on fast-to-compute, local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression as well as neural networks.

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link (url) Project Page Project Page [BibTex]


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Appealing Avatars from 3D Body Scans: Perceptual Effects of Stylization

Fleming, R., Mohler, B. J., Romero, J., Black, M. J., Breidt, M.

In Computer Vision, Imaging and Computer Graphics Theory and Applications: 11th International Joint Conference, VISIGRAPP 2016, Rome, Italy, February 27 – 29, 2016, Revised Selected Papers, pages: 175-196, Springer International Publishing, 2017 (inbook)

Abstract
Using styles derived from existing popular character designs, we present a novel automatic stylization technique for body shape and colour information based on a statistical 3D model of human bodies. We investigate whether such stylized body shapes result in increased perceived appeal with two different experiments: One focuses on body shape alone, the other investigates the additional role of surface colour and lighting. Our results consistently show that the most appealing avatar is a partially stylized one. Importantly, avatars with high stylization or no stylization at all were rated to have the least appeal. The inclusion of colour information and improvements to render quality had no significant effect on the overall perceived appeal of the avatars, and we observe that the body shape primarily drives the change in appeal ratings. For body scans with colour information, we found that a partially stylized avatar was perceived as most appealing.

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publisher site pdf DOI [BibTex]

publisher site pdf DOI [BibTex]


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Gene delivery particle engineering strategies for shape-dependent targeting of cells and tissues.

Kozielski, K., Sitti, M.

Current Gene Therapy, 17, 2017 (article)

Abstract
Background: Successful gene delivery requires overcoming both systemic and intracellular obstacles before the nucleic acid cargo can successfully reach its tissue and subcellular target location. Materials & Methods: Non-viral mechanisms to enable targeting while avoiding off-target delivery have arisen via biological, chemical, and physical engineering strategies. Discussion: Herein we will discuss the physical parameters in particle design that promote tissue- and cell-targeted delivery of genetic cargo. We will discuss systemic concerns, such as circulation, tissue localization, and clearance, as well as cell-scale obstacles, such as cellular uptake and nucleic acid packaging. Conclusion: In particular, we will focus on engineering particle shape and size in order to enhance delivery and promote precise targeting. We will also address methods to program or change particle shape in situ using environmentally triggered cues.

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DOI [BibTex]


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Multi-fractal characterization of bacterial swimming dynamics: a case study on real and simulated Serratia marcescens

Koorehdavoudi, H., Bogdan, P., Wei, G., Marculescu, R., Zhuang, J., Carlsen, R. W., Sitti, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 473(2203), The Royal Society, 2017 (article)

Abstract
To add to the current state of knowledge about bacterial swimming dynamics, in this paper, we study the fractal swimming dynamics of populations of Serratia marcescens bacteria both in vitro and in silico, while accounting for realistic conditions like volume exclusion, chemical interactions, obstacles and distribution of chemoattractant in the environment. While previous research has shown that bacterial motion is non-ergodic, we demonstrate that, besides the non-ergodicity, the bacterial swimming dynamics is multi-fractal in nature. Finally, we demonstrate that the multi-fractal characteristic of bacterial dynamics is strongly affected by bacterial density and chemoattractant concentration.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl gcpr2017 nugget
Learning to Filter Object Detections

Prokudin, S., Kappler, D., Nowozin, S., Gehler, P.

In Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland, September 12–15, 2017, Proceedings, pages: 52-62, Springer International Publishing, Cham, 2017 (inbook)

Abstract
Most object detection systems consist of three stages. First, a set of individual hypotheses for object locations is generated using a proposal generating algorithm. Second, a classifier scores every generated hypothesis independently to obtain a multi-class prediction. Finally, all scored hypotheses are filtered via a non-differentiable and decoupled non-maximum suppression (NMS) post-processing step. In this paper, we propose a filtering network (FNet), a method which replaces NMS with a differentiable neural network that allows joint reasoning and re-scoring of the generated set of hypotheses per image. This formulation enables end-to-end training of the full object detection pipeline. First, we demonstrate that FNet, a feed-forward network architecture, is able to mimic NMS decisions, despite the sequential nature of NMS. We further analyze NMS failures and propose a loss formulation that is better aligned with the mean average precision (mAP) evaluation metric. We evaluate FNet on several standard detection datasets. Results surpass standard NMS on highly occluded settings of a synthetic overlapping MNIST dataset and show competitive behavior on PascalVOC2007 and KITTI detection benchmarks.

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Paper link (url) DOI Project Page [BibTex]

Paper link (url) DOI Project Page [BibTex]


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Microemulsion-Based Soft Bacteria-Driven Microswimmers for Active Cargo Delivery

Singh, A. V., Hosseinidoust, Z., Park, B., Yasa, O., Sitti, M.

ACS Nano, 0(0):null, 2017, PMID: 28858477 (article)

Abstract
Biohybrid cell-driven microsystems offer unparalleled possibilities for realization of soft microrobots at the micron scale. Here, we introduce a bacteria-driven microswimmer that combines the active locomotion and sensing capabilities of bacteria with the desirable encapsulation and viscoelastic properties of a soft double-micelle microemulsion for active transport and delivery of cargo (e.g., imaging agents, genes, and drugs) to living cells. Quasi-monodisperse double emulsions were synthesized with an aqueous core that encapsulated the fluorescence imaging agents, as a proof-of-concept cargo in this study, and an outer oil shell that was functionalized with streptavidin for specific and stable attachment of biotin-conjugated Escherichia coli. Motile bacteria effectively propelled the soft microswimmers across a Transwell membrane, actively delivering imaging agents (i.e., dyes) encapsulated inside of the micelles to a monolayer of cultured MCF7 breast cancer and J744A.1 macrophage cells, which enabled real-time, live-cell imaging of cell organelles, namely mitochondria, endoplasmic reticulum, and Golgi body. This in vitro model demonstrates the proof-of-concept feasibility of the proposed soft microswimmers and offers promise for potential biomedical applications in active and/or targeted transport and delivery of imaging agents, drugs, stem cells, siRNA, and therapeutic genes to live tissue in in vitro disease models (e.g., organ-on-a-chip devices) and stagnant or low-flow-velocity fluidic regions of the human body.

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link (url) DOI Project Page [BibTex]


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Mode Evolution in Strongly Coupled Plasmonic Dolmens Fabricated by Templated Assembly

Flauraud, V., Bernasconi, G. D., Butet, J., Mastrangeli, M., Alexander, D. T. L., Martin, O. J. F., Brugger, J.

ACS Photonics, 4(7):1661-1668, 2017 (article)

Abstract
Plasmonic antennas have enabled a wealth of applications that exploit tailored near-fields and radiative properties, further endowed by the bespoke interactions of multiple resonant building blocks. Specifically, when the interparticle distances are reduced to a few nanometers, coupling may be greatly enhanced leading to ultimate near-field intensities and confinement along with a large energy splitting of resonant modes. While this concept is well-known, the fabrication and characterization of suitable multimers with controlled geometries and few-nanometer gaps remains highly challenging. In this article, we present the topographically templated assembly of single-crystal colloidal gold nanorods into trimers, with a dolmen geometry. This fabrication method enables the precise positioning of high-quality nanorods, with gaps as small as 1.5 nm, which permits a gradual and controlled symmetry breaking by tuning the arrangement of these strongly coupled nanostructures. To characterize the fabricated structures, we perform electron energy loss spectroscopy (EELS) near-field hyperspectral imaging and geometrically accurate EELS, plane wave, and eigenmode full-wave computations to reveal the principles governing the electromagnetic response of such nanostructures that have been extensively studied under plane wave excitation for their Fano resonant properties. These experiments track the evolution of the multipolar interactions with high accuracy as the antenna geometry varies. Our results provide new insights in strongly coupled single-crystal building blocks and open news opportunities for the design and fabrication of plasmonic systems.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Data-Driven Physics for Human Soft Tissue Animation

Kim, M., Pons-Moll, G., Pujades, S., Bang, S., Kim, J., Black, M. J., Lee, S.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):54:1-54:12, 2017 (article)

Abstract
Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

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video paper link (url) Project Page [BibTex]

video paper link (url) Project Page [BibTex]


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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.

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pdf [BibTex]

pdf [BibTex]


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Recent Advances in Skin Penetration Enhancers for Transdermal Gene and Drug Delivery

Amjadia, M., Mostaghacia, B., Sittia, M.

Current Gene Therapy, 17, 2017 (article)

Abstract
There is a growing interest in transdermal delivery systems because of their noninvasive, targeted, and on-demand delivery of gene and drugs. However, efficient penetration of therapeutic compounds into the skin is still challenging largely due to the impermeability of the outermost layer of the skin, known as stratum corneum. Recently, there have been major research activities to enhance the skin penetration depth of pharmacological agents. This article reviews recent advances in the development of various strategies for skin penetration enhancement. We show that approaches such as ultrasound waves, laser, and microneedle patches have successfully been employed to physically disrupt the stratum corneum structure for enhanced transdermal delivery. Rather than physical approaches, several non-physical route have also been utilized for efficient transdermal delivery across the skin barrier. Finally, we discuss some clinical applications of transdermal delivery systems for gene and drug delivery. This paper shows that transdermal delivery devices can potentially function for diverse healthcare and medical applications while further investigations are still necessary for more efficient skin penetration of gene and drugs.

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DOI [BibTex]


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A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

Turan, M., Pilavci, Y. Y., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.06524, 2017 (article)

Abstract
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Since the development of this technology, medical device companies and many research groups have made substantial progress in converting passive capsule endoscopes to robotic active capsule endoscopes with most of the functionality of current active flexible endoscopes. However, robotic capsule endoscopy still has some challenges. In particular, the use of such devices to generate a precise three-dimensional (3D) mapping of the entire inner organ remains an unsolved problem. Such global 3D maps of inner organs would help doctors to detect the location and size of diseased areas more accurately and intuitively, thus permitting more reliable diagnoses. To our knowledge, this paper presents the first complete pipeline for a complete 3D visual map reconstruction of the stomach. The proposed pipeline is modular and includes a preprocessing module, an image registration module, and a final shape-from-shading-based 3D reconstruction module; the 3D map is primarily generated by a combination of image stitching and shape-from-shading techniques, and is updated in a frame-by-frame iterative fashion via capsule motion inside the stomach. A comprehensive quantitative analysis of the proposed 3D reconstruction method is performed using an esophagus gastro duodenoscopy simulator, three different endoscopic cameras, and a 3D optical scanner.

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link (url) Project Page [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.

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

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


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Functionalised metal-organic frameworks: a novel approach to stabilising single metal atoms

Szilágyi, P. Á., Rogers, D. M., Zaiser, I., Callini, E., Turner, S., Borgschulte, A., Züttel, A., Geerlings, H., Hirscher, M., Dam, B.

{Journal of Materials Chemistry A}, 5(30):15559-15566, Royal Society of Chemistry, Cambridge, UK, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Exploiting diffusion barrier and chemical affinity of metal-organic frameworks for efficient hydrogen isotope separation

Kim, J. Y., Balderas-Xicohténcatl, R., Zhang, L., Kang, S. G., Hirscher, M., Oh, H., Moon, H. R.

{Journal of the American Chemical Society}, 139(42):15135-15141, American Chemical Society, Washington, DC, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Facile fabrication of mesoporous silica micro-jets with multi-functionalities

Vilela, D., Hortelao, A. C., Balderas-Xicohténcatl, R., Hirscher, M., Hahn, K., Ma, X., Sánchez, S.

{Nanoscale}, 9(37):13990-13997, Royal Society of Chemistry, Cambridge, UK, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

(Best Paper, Eurographics 2017)

Marcard, T. V., Rosenhahn, B., Black, M., Pons-Moll, G.

Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics), pages: 349-360 , 2017 (article)

Abstract
We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall

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video pdf Project Page [BibTex]

video pdf Project Page [BibTex]


Thumb xl pami 2017 teaser
Efficient 2D and 3D Facade Segmentation using Auto-Context

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

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (article)

Abstract
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.

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arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


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ClothCap: Seamless 4D Clothing Capture and Retargeting

Pons-Moll, G., Pujades, S., Hu, S., Black, M.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):73:1-73:15, ACM, New York, NY, USA, 2017, Two first authors contributed equally (article)

Abstract
Designing and simulating realistic clothing is challenging and, while several methods have addressed the capture of clothing from 3D scans, previous methods have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the naked body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. The model allows us to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes. ClothCap provides a step towards virtual try-on with a technology for capturing, modeling, and analyzing clothing in motion.

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video project_page paper link (url) DOI Project Page Project Page [BibTex]

video project_page paper link (url) DOI Project Page Project Page [BibTex]


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Mobile microrobots for bioengineering applications

Ceylan, H., Giltinan, J., Kozielski, K., Sitti, M.

Lab on a Chip, 17(10):1705-1724, Royal Society of Chemistry, 2017 (article)

Abstract
Untethered micron-scale mobile robots can navigate and non-invasively perform specific tasks inside unprecedented and hard-to-reach inner human body sites and inside enclosed organ-on-a-chip microfluidic devices with live cells. They are aimed to operate robustly and safely in complex physiological environments where they will have a transforming impact in bioengineering and healthcare. Research along this line has already demonstrated significant progress, increasing attention, and high promise over the past several years. The first-generation microrobots, which could deliver therapeutics and other cargo to targeted specific body sites, have just been started to be tested inside small animals toward clinical use. Here, we review frontline advances in design, fabrication, and testing of untethered mobile microrobots for bioengineering applications. We convey the most impactful and recent strategies in actuation, mobility, sensing, and other functional capabilities of mobile microrobots, and discuss their potential advantages and drawbacks to operate inside complex, enclosed and physiologically relevant environments. We lastly draw an outlook to provide directions in the veins of more sophisticated designs and applications, considering biodegradability, immunogenicity, mobility, sensing, and possible medical interventions in complex microenvironments.

pi

DOI [BibTex]

DOI [BibTex]


Thumb xl toc image patent
Methods, apparatuses, and systems for micromanipulation with adhesive fibrillar structures

Sitti, M., Mengüç, Y.

US Patent 9,731,422, 2017 (patent)

Abstract
The present invention are methods for fabrication of micro- and/or nano-scale adhesive fibers and their use for movement and manipulation of objects. Further disclosed is a method of manipulating a part by providing a manipulation device with a plurality of fibers, where each fiber has a tip with a flat surface that is parallel to a backing layer, contacting the flat surfaces on an object, moving the object to a new location, then disengaging the tips from the object.

pi

link (url) [BibTex]


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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.

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Code pdf DOI Project Page [BibTex]