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2014


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Bio-Hybrid Cell-Based Actuators for Microsystems

Carlsen, R. W., Sitti, M.

Small, 10(19):3831-3851, June 2014 (article)

Abstract
As we move towards the miniaturization of devices to perform tasks at the nano and microscale, it has become increasingly important to develop new methods for actuation, sensing, and control. Over the past decade, bio-hybrid methods have been investigated as a promising new approach to overcome the challenges of scaling down robotic and other functional devices. These methods integrate biological cells with artificial components and therefore, can take advantage of the intrinsic actuation and sensing functionalities of biological cells. Here, the recent advancements in bio-hybrid actuation are reviewed, and the challenges associated with the design, fabrication, and control of bio-hybrid microsystems are discussed. As a case study, focus is put on the development of bacteria-driven microswimmers, which has been investigated as a targeted drug delivery carrier. Finally, a future outlook for the development of these systems is provided. The continued integration of biological and artificial components is envisioned to enable the performance of tasks at a smaller and smaller scale in the future, leading to the parallel and distributed operation of functional systems at the microscale.

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DOI [BibTex]

2014


DOI [BibTex]


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Human Pose Estimation: New Benchmark and State of the Art Analysis

Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3686 - 3693, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

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pdf DOI Project Page Project Page Project Page [BibTex]

pdf DOI Project Page Project Page Project Page [BibTex]


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FAUST: Dataset and evaluation for 3D mesh registration

(Dataset Award, Eurographics Symposium on Geometry Processing (SGP), 2016)

Bogo, F., Romero, J., Loper, M., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3794 -3801, Columbus, Ohio, USA, IEEE International Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

Abstract
New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it. Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking. The problem is particularly challenging for non-rigid and articulated objects like human bodies. While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult. We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments. We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth. We find that current shape registration methods have trouble with this real-world data. The dataset and evaluation website are available for research purposes at http://faust.is.tue.mpg.de.

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pdf Video Dataset Poster Talk DOI Project Page Project Page Project Page [BibTex]

pdf Video Dataset Poster Talk DOI Project Page Project Page Project Page [BibTex]


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Model Transport: Towards Scalable Transfer Learning on Manifolds

Freifeld, O., Hauberg, S., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1378 -1385, Columbus, Ohio, USA, IEEE Intenational Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

Abstract
We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particular, we consider, for manifold-valued data, transfer learning of tangent-space models such as Gaussians distributions, PCA, regression, or classifiers. Though one would hope to simply use ordinary Rn-transfer learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statistics on manifolds in computer vision. At first, this straightforward idea seems to suffer from an obvious shortcoming: Transporting large datasets is prohibitively expensive, hindering scalability. Fortunately, with our approach, we never transport data. Rather, we show how the statistical models themselves can be transported, and prove that for the tangent-space models above, the transport “commutes” with learning. Consequently, our compact framework, applicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors.

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pdf SupMat Video poster DOI Project Page [BibTex]

pdf SupMat Video poster DOI Project Page [BibTex]


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Robot Arm Pose Estimation through Pixel-Wise Part Classification

Bohg, J., Romero, J., Herzog, A., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA) 2014, pages: 3143-3150, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (inproceedings)

Abstract
We propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data.

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video code pdf DOI Project Page [BibTex]

video code pdf DOI Project Page [BibTex]


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Efficient Non-linear Markov Models for Human Motion

Lehrmann, A. M., Gehler, P. V., Nowozin, S.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1314-1321, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

Abstract
Dynamic Bayesian networks such as Hidden Markov Models (HMMs) are successfully used as probabilistic models for human motion. The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods. In this work we propose to instead use simple Markov models that only model observed quantities. We retain a highly expressive dynamic model by using interactions that are nonlinear and non-parametric. A presentation of our approach in terms of latent variables shows logarithmic growth for the computation of exact loglikelihoods in the number of latent states. We validate our model on human motion capture data and demonstrate state-of-the-art performance on action recognition and motion completion tasks.

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Project page pdf DOI Project Page [BibTex]

Project page pdf DOI Project Page [BibTex]


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Grassmann Averages for Scalable Robust PCA

Hauberg, S., Feragen, A., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3810 -3817, Columbus, Ohio, USA, IEEE International Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

Abstract
As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase – "big data" implies "big outliers". While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA do not scale beyond small-to-medium sized datasets. To address this, we introduce the Grassmann Average (GA), which expresses dimensionality reduction as an average of the subspaces spanned by the data. Because averages can be efficiently computed, we immediately gain scalability. GA is inherently more robust than PCA, but we show that they coincide for Gaussian data. We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. Robustness can be with respect to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements, making it scalable to "big noisy data." We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie.

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pdf code supplementary material tutorial video results video talk poster DOI Project Page [BibTex]

pdf code supplementary material tutorial video results video talk poster DOI Project Page [BibTex]


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Posebits for Monocular Human Pose Estimation

Pons-Moll, G., Fleet, D. J., Rosenhahn, B.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 2345-2352, Columbus, Ohio, USA, IEEE International Conference on Computer Vision and Pattern Recognition, June 2014 (inproceedings)

Abstract
We advocate the inference of qualitative information about 3D human pose, called posebits, from images. Posebits represent boolean geometric relationships between body parts (e.g., left-leg in front of right-leg or hands close to each other). The advantages of posebits as a mid-level representation are 1) for many tasks of interest, such qualitative pose information may be sufficient (e.g. , semantic image retrieval), 2) it is relatively easy to annotate large image corpora with posebits, as it simply requires answers to yes/no questions; and 3) they help resolve challenging pose ambiguities and therefore facilitate the difficult talk of image-based 3D pose estimation. We introduce posebits, a posebit database, a method for selecting useful posebits for pose estimation and a structural SVM model for posebit inference. Experiments show the use of posebits for semantic image retrieval and for improving 3D pose estimation.

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pdf Project Page Project Page [BibTex]

pdf Project Page Project Page [BibTex]


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Simultaneous Underwater Visibility Assessment, Enhancement and Improved Stereo

Roser, M., Dunbabin, M., Geiger, A.

IEEE International Conference on Robotics and Automation, pages: 3840 - 3847 , Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

Abstract
Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows real-time implementation onboard an Autonomous Underwater Vehicles for improved navigation and obstacle avoidance performance.

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pdf DOI [BibTex]

pdf DOI [BibTex]


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Preserving Modes and Messages via Diverse Particle Selection

Pacheco, J., Zuffi, S., Black, M. J., Sudderth, E.

In Proceedings of the 31st International Conference on Machine Learning (ICML-14), 32(1):1152-1160, J. Machine Learning Research Workshop and Conf. and Proc., Beijing, China, International Conference on Machine Learning (ICML), June 2014 (inproceedings)

Abstract
In applications of graphical models arising in domains such as computer vision and signal processing, we often seek the most likely configurations of high-dimensional, continuous variables. We develop a particle-based max-product algorithm which maintains a diverse set of posterior mode hypotheses, and is robust to initialization. At each iteration, the set of hypotheses at each node is augmented via stochastic proposals, and then reduced via an efficient selection algorithm. The integer program underlying our optimization-based particle selection minimizes errors in subsequent max-product message updates. This objective automatically encourages diversity in the maintained hypotheses, without requiring tuning of application-specific distances among hypotheses. By avoiding the stochastic resampling steps underlying particle sum-product algorithms, we also avoid common degeneracies where particles collapse onto a single hypothesis. Our approach significantly outperforms previous particle-based algorithms in experiments focusing on the estimation of human pose from single images.

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pdf SupMat link (url) Project Page Project Page [BibTex]

pdf SupMat link (url) Project Page Project Page [BibTex]


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Calibrating and Centering Quasi-Central Catadioptric Cameras

Schoenbein, M., Strauss, T., Geiger, A.

IEEE International Conference on Robotics and Automation, pages: 4443 - 4450, Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

Abstract
Non-central catadioptric models are able to cope with irregular camera setups and inaccuracies in the manufacturing process but are computationally demanding and thus not suitable for robotic applications. On the other hand, calibrating a quasi-central (almost central) system with a central model introduces errors due to a wrong relationship between the viewing ray orientations and the pixels on the image sensor. In this paper, we propose a central approximation to quasi-central catadioptric camera systems that is both accurate and efficient. We observe that the distance to points in 3D is typically large compared to deviations from the single viewpoint. Thus, we first calibrate the system using a state-of-the-art non-central camera model. Next, we show that by remapping the observations we are able to match the orientation of the viewing rays of a much simpler single viewpoint model with the true ray orientations. While our approximation is general and applicable to all quasi-central camera systems, we focus on one of the most common cases in practice: hypercatadioptric cameras. We compare our model to a variety of baselines in synthetic and real localization and motion estimation experiments. We show that by using the proposed model we are able to achieve near non-central accuracy while obtaining speed-ups of more than three orders of magnitude compared to state-of-the-art non-central models.

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pdf DOI [BibTex]

pdf DOI [BibTex]


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3D Traffic Scene Understanding from Movable Platforms

Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, published, IEEE, Los Alamitos, CA, May 2014 (article)

Abstract
In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow and occupancy grids. For each of these cues we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.

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pdf link (url) [BibTex]

pdf link (url) [BibTex]


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Modeling the Human Body in 3D: Data Registration and Human Shape Representation

Tsoli, A.

Brown University, Department of Computer Science, May 2014 (phdthesis)

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pdf [BibTex]

pdf [BibTex]


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Model transport: towards scalable transfer learning on manifolds - supplemental material

Freifeld, O., Hauberg, S., Black, M. J.

(9), April 2014 (techreport)

Abstract
This technical report is complementary to "Model Transport: Towards Scalable Transfer Learning on Manifolds" and contains proofs, explanation of the attached video (visualization of bases from the body shape experiments), and high-resolution images of select results of individual reconstructions from the shape experiments. It is identical to the supplemental mate- rial submitted to the Conference on Computer Vision and Pattern Recognition (CVPR 2014) on November 2013.

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PDF [BibTex]


Thumb xl aistats2014
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Hennig, P., Hauberg, S.

In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

Abstract
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtually all computations. The probabilistic formulation permits marginalising the uncertainty of the numerical solution such that statistics are less sensitive to inaccuracies. This leads to new Riemannian algorithms for mean value computations and principal geodesic analysis. Marginalisation also means results can be less precise than point estimates, enabling a noticeable speed-up over the state of the art. Our approach is an argument for a wider point that uncertainty caused by numerical calculations should be tracked throughout the pipeline of machine learning algorithms.

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pdf Youtube Supplements Project page link (url) [BibTex]

pdf Youtube Supplements Project page link (url) [BibTex]


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Multi-View Priors for Learning Detectors from Sparse Viewpoint Data

Pepik, B., Stark, M., Gehler, P., Schiele, B.

International Conference on Learning Representations, International Conference on Learning Representations (ICLR), April 2014 (conference)

Abstract
While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate. However, models trained to provide richer output require larger amounts of training data, preferably well covering the relevant aspects such as viewpoint and fine-grained categories. In this paper, we address this issue from the perspective of transfer learning, and design an object class model that explicitly leverages correlations between visual features. Specifically, our model represents prior distributions over permissible multi-view detectors in a parametric way -- the priors are learned once from training data of a source object class, and can later be used to facilitate the learning of a detector for a target class. As we show in our experiments, this transfer is not only beneficial for detectors based on basic-level category representations, but also enables the robust learning of detectors that represent classes at finer levels of granularity, where training data is typically even scarcer and more unbalanced. As a result, we report largely improved performance in simultaneous 2D object localization and viewpoint estimation on a recent dataset of challenging street scenes.

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reviews pdf Project Page [BibTex]

reviews pdf Project Page [BibTex]


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Fibrillar structures to reduce viscous drag on aerodynamic and hydrodynamic wall surfaces

Castillo, L., Aksak, B., Sitti, M.

March 2014, US Patent App. 14/774,767 (misc)

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[BibTex]

[BibTex]


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Adaptive Offset Correction for Intracortical Brain Computer Interfaces

Homer, M. L., Perge, J. A., Black, M. J., Harrison, M. T., Cash, S. S., Hochberg, L. R.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2):239-248, March 2014 (article)

Abstract
Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ± 10.1\%; p < 0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Model-based Anthropometry: Predicting Measurements from 3D Human Scans in Multiple Poses

Tsoli, A., Loper, M., Black, M. J.

In Proceedings Winter Conference on Applications of Computer Vision, pages: 83-90, IEEE , IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2014 (inproceedings)

Abstract
Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custom clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution we call model-based anthropometry. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration with a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-the-art methods.

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pdf DOI Project Page Project Page [BibTex]

pdf DOI Project Page Project Page [BibTex]


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The design of microfibers with mushroom-shaped tips for optimal adhesion

Sitti, M., Aksak, B.

February 2014, US Patent App. 14/766,561 (misc)

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[BibTex]

[BibTex]


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A freely-moving monkey treadmill model

Foster, J., Nuyujukian, P., Freifeld, O., Gao, H., Walker, R., Ryu, S., Meng, T., Murmann, B., Black, M., Shenoy, K.

J. of Neural Engineering, 11(4):046020, 2014 (article)

Abstract
Objective: Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. Approach: We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the excitability and utility of this new monkey model, including the fi rst recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Main results: Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average ring rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at diff erent speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Significance: Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment, and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

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pdf Supplementary DOI Project Page [BibTex]

pdf Supplementary DOI Project Page [BibTex]


Thumb xl publications toccontinuously distributed
Continuously distributed magnetization profile for millimeter-scale elastomeric undulatory swimming

Diller, E., Zhuang, J., Zhan Lum, G., Edwards, M. R., Sitti, M.

Applied Physics Letters, 104(17):174101, AIP, 2014 (article)

Abstract
We have developed a millimeter-scale magnetically driven swimming robot for untethered motion at mid to low Reynolds numbers. The robot is propelled by continuous undulatory deformation, which is enabled by the distributed magnetization profile of a flexible sheet. We demonstrate control of a prototype device and measure deformation and speed as a function of magnetic field strength and frequency. Experimental results are compared with simple magnetoelastic and fluid propulsion models. The presented mechanism provides an efficient remote actuation method at the millimeter scale that may be suitable for further scaling down in size for microrobotics applications in biotechnology and healthcare

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Biopsy using a Magnetic Capsule Endoscope Carrying, Releasing and Retrieving Untethered Micro-Grippers

Yim, S., Gultepe, E., Gracias, D. H., Sitti, M.

IEEE Trans. on Biomedical Engineering, 61(2):513-521, IEEE, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Investigation of tip current and normal force measured simultaneously during local oxidation of titanium using dual-mode scanning probe microscopy

Ozcan, O., Hu, W., Sitti, M., Bain, J., Ricketts, D.

IET Micro \& Nano Letters, 9(5):332-336, IET, 2014 (article)

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[BibTex]

[BibTex]


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Three-dimensional robotic manipulation and transport of micro-scale objects by a magnetically driven capillary micro-gripper

Giltinan, J., Diller, E., Mayda, C., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2077-2082, 2014 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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SoftCubes: Stretchable and self-assembling three-dimensional soft modular matter

Yim, S., Sitti, M.

The International Journal of Robotics Research, 33(8):1083-1097, SAGE Publications Sage UK: London, England, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Bio-Hybrid Cell-Based Actuators for Microsystems

Carlsen, Rika Wright, Sitti, Metin

Small, 10(19):3831-3851, 2014 (article)

Abstract
As we move towards the miniaturization of devices to perform tasks at the nano and microscale, it has become increasingly important to develop new methods for actuation, sensing, and control. Over the past decade, bio-hybrid methods have been investigated as a promising new approach to overcome the challenges of scaling down robotic and other functional devices. These methods integrate biological cells with artificial components and therefore, can take advantage of the intrinsic actuation and sensing functionalities of biological cells. Here, the recent advancements in bio-hybrid actuation are reviewed, and the challenges associated with the design, fabrication, and control of bio-hybrid microsystems are discussed. As a case study, focus is put on the development of bacteria-driven microswimmers, which has been investigated as a targeted drug delivery carrier. Finally, a future outlook for the development of these systems is provided. The continued integration of biological and artificial components is envisioned to enable the performance of tasks at a smaller and smaller scale in the future, leading to the parallel and distributed operation of functional systems at the microscale.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Addressing of Micro-robot Teams and Non-contact Micro-manipulation

Diller, E., Ye, Z., Giltinan, J., Sitti, M.

In Small-Scale Robotics. From Nano-to-Millimeter-Sized Robotic Systems and Applications, pages: 28-38, Springer Berlin Heidelberg, 2014 (incollection)

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Project Page [BibTex]

Project Page [BibTex]


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Staying sticky: contact self-cleaning of gecko-inspired adhesives

Mengüç, Y., Röhrig, M., Abusomwan, U., Hölscher, H., Sitti, M.

Journal of The Royal Society Interface, 11(94):20131205, The Royal Society, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Dynamic Trapping and Two-Dimensional Transport of Swimming Microorganisms Using a Rotating Magnetic Micro-Robot

Ye, Z., Sitti, M.

Lab on a Chip, 14(13):2177-2182, Royal Society of Chemistry, 2014 (article)

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Project Page [BibTex]


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STRIDE II: a water strider-inspired miniature robot with circular footpads

Ozcan, O., Wang, H., Taylor, J. D., Sitti, M.

International Journal of Advanced Robotic Systems, 11(6):85, SAGE Publications Sage UK: London, England, 2014 (article)

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[BibTex]

[BibTex]


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Soft Grippers Using Micro-Fibrillar Adhesives for Transfer Printing

Song, S., Sitti, M.

Advanced Materials, 26(28):4901-4906, 2014 (article)

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[BibTex]

[BibTex]


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Can DC motors directly drive flapping wings at high frequency and large wing strokes?

Campolo, D., Azhar, M., Lau, G., Sitti, M.

IEEE/ASME Trans. on Mechatronics, 19(1):109-120, 2014 (article)

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[BibTex]

[BibTex]


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Magnetic steering control of multi-cellular bio-hybrid microswimmers

Carlsen, R. W., Edwards, M. R., Zhuang, J., Pacoret, C., Sitti, M.

Lab on a Chip, 14(19):3850-3859, Royal Society of Chemistry, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Analytical modeling and experimental characterization of chemotaxis in serratia marcescens

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

Physical Review E, 89(5):052704, American Physical Society, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Robotic assembly of hydrogels for tissue engineering and regenerative medicine

Tasoglu, S, Diller, E, Guven, S, Sitti, M, Demirci, U

In Journal of Tissue Engineering and Regenerative Medicine, 8, pages: 181-182, 2014 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Swimming characterization of Serratia marcescens for bio-hybrid micro-robotics

Edwards, M. R., Carlsen, R. W., Zhuang, J., Sitti, M.

Journal of Micro-Bio Robotics, 9(3):47-60, Springer Berlin Heidelberg, 2014 (article)

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[BibTex]

[BibTex]


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Influence of Magnetic Fields on Magneto-Aerotaxis

Bennet, M., McCarthy, A., Fix, D., Edwards, M. R., Repp, F., Vach, P., Dunlop, J. W., Sitti, M., Buller, G. S., Klumpp, S., others,

PLoS One, 9(7):e101150, Public Library of Science, 2014 (article)

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[BibTex]

[BibTex]


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Liftoff of a Motor-Driven, Flapping-Wing Microaerial Vehicle Capable of Resonance

Hines, L., Campolo, D., Sitti, M.

IEEE Trans. on Robotics, 30(1):220-232, IEEE, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Untethered micro-robotic coding of three-dimensional material composition

Tasoglu, S, Diller, E, Guven, S, Sitti, M, Demirci, U

Nature Communications, 5, pages: DOI-10, Nature Publishing Group, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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The optimal shape of elastomer mushroom-like fibers for high and robust adhesion

Aksak, B., Sahin, K., Sitti, M.

Beilstein journal of nanotechnology, 5(1):630-638, Beilstein-Institut, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Mechanically Switchable Elastomeric Microfibrillar Adhesive Surfaces for Transfer Printing

Sariola, V., Sitti, M.

Advanced Materials Interfaces, 1(4):1300159, 2014 (article)

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[BibTex]

[BibTex]


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Versatile non-contact micro-manipulation method using rotational flows locally induced by magnetic microrobots

Ye, Z., Edington, C., Russell, A. J., Sitti, M.

In Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference on, pages: 26-31, 2014 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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MultiMo-Bat: A biologically inspired integrated jumping–gliding robot

Woodward, M. A., Sitti, M.

The International Journal of Robotics Research, 33(12):1511-1529, SAGE Publications Sage UK: London, England, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


Thumb xl simulated annealing
Simulated Annealing

Gall, J.

In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)

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[BibTex]

[BibTex]


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A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them

Sun, D., Roth, S., Black, M. J.

International Journal of Computer Vision (IJCV), 106(2):115-137, 2014 (article)

Abstract
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical'' flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

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pdf full text code [BibTex]

pdf full text code [BibTex]


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Rotating Magnetic Miniature Swimming Robots With Multiple Flexible Flagella

Ye, Z., Régnier, S., Sitti, M.

IEEE Trans. on Robotics, 30(1):3-13, 2014 (article)

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[BibTex]

[BibTex]


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Structural optimization method towards synthesis of small scale flexure-based mobile grippers

Lum, G. Z., Diller, E., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2339-2344, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Three-Dimensional Programmable Assembly by Untethered Magnetic Robotic Micro-Grippers

Diller, E., Sitti, M.

Advanced Functional Materials, 24, pages: 4397-4404, 2014 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Six-Degrees-of-Freedom Remote Actuation of Magnetic Microrobots.

Diller, E. D., Giltinan, J., Lum, G. Z., Ye, Z., Sitti, M.

In Robotics: Science and Systems, 2014 (inproceedings)

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[BibTex]

[BibTex]