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2018


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Instrumentation, Data, and Algorithms for Visually Understanding Haptic Surface Properties

Burka, A. L.

University of Pennsylvania, Philadelphia, USA, August 2018, Department of Electrical and Systems Engineering (phdthesis)

Abstract
Autonomous robots need to efficiently walk over varied surfaces and grasp diverse objects. We hypothesize that the association between how such surfaces look and how they physically feel during contact can be learned from a database of matched haptic and visual data recorded from various end-effectors' interactions with hundreds of real-world surfaces. Testing this hypothesis required the creation of a new multimodal sensing apparatus, the collection of a large multimodal dataset, and development of a machine-learning pipeline. This thesis begins by describing the design and construction of the Portable Robotic Optical/Tactile ObservatioN PACKage (PROTONPACK, or Proton for short), an untethered handheld sensing device that emulates the capabilities of the human senses of vision and touch. Its sensory modalities include RGBD vision, egomotion, contact force, and contact vibration. Three interchangeable end-effectors (a steel tooling ball, an OptoForce three-axis force sensor, and a SynTouch BioTac artificial fingertip) allow for different material properties at the contact point and provide additional tactile data. We then detail the calibration process for the motion and force sensing systems, as well as several proof-of-concept surface discrimination experiments that demonstrate the reliability of the device and the utility of the data it collects. This thesis then presents a large-scale dataset of multimodal surface interaction recordings, including 357 unique surfaces such as furniture, fabrics, outdoor fixtures, and items from several private and public material sample collections. Each surface was touched with one, two, or three end-effectors, comprising approximately one minute per end-effector of tapping and dragging at various forces and speeds. We hope that the larger community of robotics researchers will find broad applications for the published dataset. Lastly, we demonstrate an algorithm that learns to estimate haptic surface properties given visual input. Surfaces were rated on hardness, roughness, stickiness, and temperature by the human experimenter and by a pool of purely visual observers. Then we trained an algorithm to perform the same task as well as infer quantitative properties calculated from the haptic data. Overall, the task of predicting haptic properties from vision alone proved difficult for both humans and computers, but a hybrid algorithm using a deep neural network and a support vector machine achieved a correlation between expected and actual regression output between approximately ρ = 0.3 and ρ = 0.5 on previously unseen surfaces.

hi

Project Page [BibTex]

2018


Project Page [BibTex]


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Robust Visual Augmented Reality in Robot-Assisted Surgery

Forte, M. P.

Politecnico di Milano, Milan, Italy, July 2018, Department of Electronic, Information, and Biomedical Engineering (mastersthesis)

Abstract
The broader research objective of this line of research is to test the hypothesis that real-time stereo video analysis and augmented reality can increase safety and task efficiency in robot-assisted surgery. This master’s thesis aims to solve the first step needed to achieve this goal: the creation of a robust system that delivers the envisioned feedback to a surgeon while he or she controls a surgical robot that is identical to those used on human patients. Several approaches for applying augmented reality to da Vinci Surgical Systems have been proposed, but none of them entirely rely on a clinical robot; specifically, they require additional sensors, depend on access to the da Vinci API, are designed for a very specific task, or were tested on systems that are starkly different from those in clinical use. There has also been prior work that presents the real-world camera view and the computer graphics on separate screens, or not in real time. In other scenarios, the digital information is overlaid manually by the surgeons themselves or by computer scientists, rather than being generated automatically in response to the surgeon’s actions. We attempted to overcome the aforementioned constraints by acquiring input signals from the da Vinci stereo endoscope and providing augmented reality to the console in real time (less than 150 ms delay, including the 62 ms of inherent latency of the da Vinci). The potential benefits of the resulting system are broad because it was built to be general, rather than customized for any specific task. The entire platform is compatible with any generation of the da Vinci System and does not require a dVRK (da Vinci Research Kit) or access to the API. Thus, it can be applied to existing da Vinci Systems in operating rooms around the world.

hi

Project Page [BibTex]

Project Page [BibTex]


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Colloidal Chemical Nanomotors

Alarcon-Correa, M.

Colloidal Chemical Nanomotors, pages: 150, Cuvillier Verlag, MPI-IS , June 2018 (phdthesis)

Abstract
Synthetic sophisticated nanostructures represent a fundamental building block for the development of nanotechnology. The fabrication of nanoparticles complex in structure and material composition is key to build nanomachines that can operate as man-made nanoscale motors, which autonomously convert external energy into motion. To achieve this, asymmetric nanoparticles were fabricated combining a physical vapor deposition technique known as NanoGLAD and wet chemical synthesis. This thesis primarily concerns three complex colloidal systems that have been developed: i)Hollow nanocup inclusion complexes that have a single Au nanoparticle in their pocket. The Au particle can be released with an external trigger. ii)The smallest self-propelling nanocolloids that have been made to date, which give rise to a local concentration gradient that causes enhanced diffusion of the particles. iii)Enzyme-powered pumps that have been assembled using bacteriophages as biological nanoscaffolds. This construct also can be used for enzyme recovery after heterogeneous catalysis.

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[BibTex]

[BibTex]


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Impact of Trunk Orientation for Dynamic Bipedal Locomotion

Drama, O.

Dynamic Walking Conference, May 2018 (talk)

Abstract
Impact of trunk orientation for dynamic bipedal locomotion My research revolves around investigating the functional demands of bipedal running, with focus on stabilizing trunk orientation. When we think about postural stability, there are two critical questions we need to answer: What are the necessary and sufficient conditions to achieve and maintain trunk stability? I am concentrating on how morphology affects control strategies in achieving trunk stability. In particular, I denote the trunk pitch as the predominant morphology parameter and explore the requirements it imposes on a chosen control strategy. To analyze this, I use a spring loaded inverted pendulum model extended with a rigid trunk, which is actuated by a hip motor. The challenge for the controller design here is to have a single hip actuator to achieve two coupled tasks of moving the legs to generate motion and stabilizing the trunk. I enforce orthograde and pronograde postures and aim to identify the effect of these trunk orientations on the hip torque and ground reaction profiles for different control strategies.

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Impact of trunk orientation for dynamic bipedal locomotion [DW 2018] link (url) Project Page [BibTex]


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Model-based Optical Flow: Layers, Learning, and Geometry

Wulff, J.

Tuebingen University, April 2018 (phdthesis)

Abstract
The estimation of motion in video sequences establishes temporal correspondences between pixels and surfaces and allows reasoning about a scene using multiple frames. Despite being a focus of research for over three decades, computing motion, or optical flow, remains challenging due to a number of difficulties, including the treatment of motion discontinuities and occluded regions, and the integration of information from more than two frames. One reason for these issues is that most optical flow algorithms only reason about the motion of pixels on the image plane, while not taking the image formation pipeline or the 3D structure of the world into account. One approach to address this uses layered models, which represent the occlusion structure of a scene and provide an approximation to the geometry. The goal of this dissertation is to show ways to inject additional knowledge about the scene into layered methods, making them more robust, faster, and more accurate. First, this thesis demonstrates the modeling power of layers using the example of motion blur in videos, which is caused by fast motion relative to the exposure time of the camera. Layers segment the scene into regions that move coherently while preserving their occlusion relationships. The motion of each layer therefore directly determines its motion blur. At the same time, the layered model captures complex blur overlap effects at motion discontinuities. Using layers, we can thus formulate a generative model for blurred video sequences, and use this model to simultaneously deblur a video and compute accurate optical flow for highly dynamic scenes containing motion blur. Next, we consider the representation of the motion within layers. Since, in a layered model, important motion discontinuities are captured by the segmentation into layers, the flow within each layer varies smoothly and can be approximated using a low dimensional subspace. We show how this subspace can be learned from training data using principal component analysis (PCA), and that flow estimation using this subspace is computationally efficient. The combination of the layered model and the low-dimensional subspace gives the best of both worlds, sharp motion discontinuities from the layers and computational efficiency from the subspace. Lastly, we show how layered methods can be dramatically improved using simple semantics. Instead of treating all layers equally, a semantic segmentation divides the scene into its static parts and moving objects. Static parts of the scene constitute a large majority of what is shown in typical video sequences; yet, in such regions optical flow is fully constrained by the depth structure of the scene and the camera motion. After segmenting out moving objects, we consider only static regions, and explicitly reason about the structure of the scene and the camera motion, yielding much better optical flow estimates. Furthermore, computing the structure of the scene allows to better combine information from multiple frames, resulting in high accuracies even in occluded regions. For moving regions, we compute the flow using a generic optical flow method, and combine it with the flow computed for the static regions to obtain a full optical flow field. By combining layered models of the scene with reasoning about the dynamic behavior of the real, three-dimensional world, the methods presented herein push the envelope of optical flow computation in terms of robustness, speed, and accuracy, giving state-of-the-art results on benchmarks and pointing to important future research directions for the estimation of motion in natural scenes.

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Official link DOI Project Page [BibTex]


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A virtual reality environment for experiments in assistive robotics and neural interfaces

Bustamante, S.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Optimal Trajectory Generation and Learning Control for Robot Table Tennis

Koc, O.

Technical University Darmstadt, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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Distribution-Dissimilarities in Machine Learning

Simon-Gabriel, C. J.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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A Causal Perspective on Deep Representation Learning

Suter, R.

ETH Zurich, 2018 (mastersthesis)

ei

[BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

[BibTex]

[BibTex]


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A machine learning approach to taking EEG-based computer interfaces out of the lab

Jayaram, V.

Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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XMCD investigations on new hard magnetic systems

Chen, Y.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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High-Resolution X-ray Ptychography for Magnetic Imaging

Bykova, I.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2016


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Non-parametric Models for Structured Data and Applications to Human Bodies and Natural Scenes

Lehrmann, A.

ETH Zurich, July 2016 (phdthesis)

Abstract
The purpose of this thesis is the study of non-parametric models for structured data and their fields of application in computer vision. We aim at the development of context-sensitive architectures which are both expressive and efficient. Our focus is on directed graphical models, in particular Bayesian networks, where we combine the flexibility of non-parametric local distributions with the efficiency of a global topology with bounded treewidth. A bound on the treewidth is obtained by either constraining the maximum indegree of the underlying graph structure or by introducing determinism. The non-parametric distributions in the nodes of the graph are given by decision trees or kernel density estimators. The information flow implied by specific network topologies, especially the resultant (conditional) independencies, allows for a natural integration and control of contextual information. We distinguish between three different types of context: static, dynamic, and semantic. In four different approaches we propose models which exhibit varying combinations of these contextual properties and allow modeling of structured data in space, time, and hierarchies derived thereof. The generative character of the presented models enables a direct synthesis of plausible hypotheses. Extensive experiments validate the developed models in two application scenarios which are of particular interest in computer vision: human bodies and natural scenes. In the practical sections of this work we discuss both areas from different angles and show applications of our models to human pose, motion, and segmentation as well as object categorization and localization. Here, we benefit from the availability of modern datasets of unprecedented size and diversity. Comparisons to traditional approaches and state-of-the-art research on the basis of well-established evaluation criteria allows the objective assessment of our contributions.

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pdf [BibTex]


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Screening Rules for Convex Problems

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.

2016 (unpublished) Submitted

ei

[BibTex]

[BibTex]


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Statische und dynamische Magnetisierungseigenschaften nanoskaliger Überstrukturen

Gräfe, J.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

[BibTex]

[BibTex]


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Gepinnte Bahnmomente in magnetischen Heterostrukturen

Audehm, P.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

[BibTex]

[BibTex]


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Austauschgekoppelte Moden in magnetischen Vortexstrukturen

Dieterle, G.

Universität Stuttgart, Stuttgart, 2016 (phdthesis)

mms

[BibTex]

[BibTex]


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Density matrix calculations for the ultrafast demagnetization after femtosecond laser pulses

Weng, Weikai

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

mms

[BibTex]

[BibTex]


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Deep Learning for Diabetic Retinopathy Diagnostics

Balles, Lukas

Heidelberg University, 2016 (mastersthesis)

[BibTex]

[BibTex]


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Helium und Hydrogen Isotope Adsorption and Separation in Metal-Organic Frameworks

Zaiser, Ingrid

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

[BibTex]

[BibTex]

2010


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Comparative Quantitative Evaluation of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

Mantlik, F., Hofmann, M., Bezrukov, I., Kolb, A., Beyer, T., Reimold, M., Pichler, B., Schölkopf, B.

2010(M08-4), 2010 Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC), November 2010 (talk)

Abstract
Combined PET/MR provides at the same time molecular and functional imaging as well as excellent soft tissue contrast. It does not allow one to directly measure the attenuation properties of scanned tissues, despite the fact that accurate attenuation maps are necessary for quantitative PET imaging. Several methods have therefore been proposed for MR-based attenuation correction (MR-AC). So far, they have only been evaluated on data acquired from separate MR and PET scanners. We evaluated several MR-AC methods on data from 10 patients acquired on a combined BrainPET/MR scanner. This allowed the consideration of specific PET/MR issues, such as the RF coil that attenuates and scatters 511 keV gammas. We evaluated simple MR thresholding methods as well as atlas and machine learning-based MR-AC. CT-based AC served as gold standard reference. To comprehensively evaluate the MR-AC accuracy, we used RoIs from 2 anatomic brain atlases with different levels of detail. Visual inspection of the PET images indicated that even the basic FLASH threshold MR-AC may be sufficient for several applications. Using a UTE sequence for bone prediction in MR-based thresholding occasionally led to false prediction of bone tissue inside the brain, causing a significant overestimation of PET activity. Although it yielded a lower mean underestimation of activity, it exhibited the highest variance of all methods. The atlas averaging approach had a smaller mean error, but showed high maximum overestimation on the RoIs of the more detailed atlas. The Nave Bayes and Atlas-Patch MR-AC yielded the smallest variance, and the Atlas-Patch also showed the smallest mean error. In conclusion, Atlas-based AC using only MR information on the BrainPET/MR yields a high level of accuracy that is sufficient for clinical quantitative imaging requirements. The Atlas-Patch approach was superior to alternative atlas-based methods, yielding a quantification error below 10% for all RoIs except very small ones.

ei

[BibTex]

2010


[BibTex]


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Bayesian Inference and Experimental Design for Large Generalised Linear Models

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Statistical image analysis and percolation theory

Davies, P., Langovoy, M., Wittich, O.

73rd Annual Meeting of the Institute of Mathematical Statistics (IMS), August 2010 (talk)

Abstract
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures.

ei

Web [BibTex]

Web [BibTex]


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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical image analysis and percolation theory

Langovoy, M., Wittich, O.

28th European Meeting of Statisticians (EMS), August 2010 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

24th European Conference on Operational Research (EURO XXIV), July 2010 (talk)

Abstract
We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of edges: the cost of an edge that is added to a cut set depends on the edges in the set. Applications are e.g. in probabilistic graphical models and image processing. We prove NP hardness and a polynomial lower bound on the approximation factor, and upper bounds via four approximation algorithms based on different techniques. Our additional heuristics have attractive practical properties, e.g., to rely only on standard min-cut. Both our algorithms and heuristics appear to do well in practice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Solving Large-Scale Nonnegative Least Squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Han- son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by ex- ploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established con- vex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Matrix Approximation Problems

Sra, S.

EU Regional School: Rheinisch-Westf{\"a}lische Technische Hochschule Aachen, May 2010 (talk)

ei

PDF AVI [BibTex]

PDF AVI [BibTex]


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BCI2000 and Python

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract
A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 realtime software package.

ei

PDF [BibTex]

PDF [BibTex]


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Extending BCI2000 Functionality with Your Own C++ Code

Hill, NJ.

Invited lecture at the 7th International BCI2000 Workshop, Pacific Grove, CA, USA, May 2010 (talk)

Abstract
A tutorial, with exercises, on how to use BCI2000 C++ framework to write your own real-time signal-processing modules.

ei

[BibTex]

[BibTex]


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Machine-Learning Methods for Decoding Intentional Brain States

Hill, NJ.

Symposium "Non-Invasive Brain Computer Interfaces: Current Developments and Applications" (BIOMAG), March 2010 (talk)

Abstract
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the user‘s initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since “it doesn‘t matter what classifier you use once your features are extracted.” Using examples from our own MEG and EEG experiments, I‘ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than “just” classification, and can be used to find better feature extractors.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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PAC-Bayesian Analysis in Unsupervised Learning

Seldin, Y.

Foundations and New Trends of PAC Bayesian Learning Workshop, March 2010 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

ei

[BibTex]

[BibTex]


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JMLR Workshop and Conference Proceedings: Volume 6

Guyon, I., Janzing, D., Schölkopf, B.

pages: 288, MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (proceedings)

ei

Web [BibTex]

Web [BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J.

EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality, January 2010 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei

[BibTex]

[BibTex]