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2020


SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS
SIMULTANEOUS CALIBRATION METHOD FOR MAGNETIC LOCALIZATION AND ACTUATION SYSTEMS

Sitti, M., Son, D., Dong, X.

June 2020, US Patent App. 16/696,605 (misc)

Abstract
The invention relates to a method of simultaneously calibrating magnetic actuation and sensing systems for a workspace, wherein the actuation system comprises a plurality of magnetic actuators and the sensing system comprises a plurality of magnetic sensors, wherein all the measured data is fed into a calibration model, wherein the calibration model is based on a sensor measurement model and a magnetic actuation model, and wherein a solution of the model parameters is found via a numerical solver order to calibrate both the actuation and sensing systems at the same time.

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[BibTex]


Bayesian Optimization in Robot Learning - Automatic Controller Tuning and Sample-Efficient Methods
Bayesian Optimization in Robot Learning - Automatic Controller Tuning and Sample-Efficient Methods

Marco-Valle, A.

University of Tübingen, June 2020 (thesis)

Abstract
The problem of designing controllers to regulate dynamical systems has been studied by engineers during the past millennia. Ever since, suboptimal performance lingers in many closed loops as an unavoidable side effect of manually tuning the parameters of the controllers. Nowadays, industrial settings remain skeptic about data-driven methods that allow one to automatically learn controller parameters. In the context of robotics, machine learning (ML) keeps growing its influence on increasing autonomy and adaptability, for example to aid automating controller tuning. However, data-hungry ML methods, such as standard reinforcement learning, require a large number of experimental samples, prohibitive in robotics, as hardware can deteriorate and break. This brings about the following question: Can manual controller tuning, in robotics, be automated by using data-efficient machine learning techniques? In this thesis, we tackle the question above by exploring Bayesian optimization (BO), a data-efficient ML framework, to buffer the human effort and side effects of manual controller tuning, while retaining a low number of experimental samples. We focus this work in the context of robotic systems, providing thorough theoretical results that aim to increase data-efficiency, as well as demonstrations in real robots. Specifically, we present four main contributions. We first consider using BO to replace manual tuning in robotic platforms. To this end, we parametrize the design weights of a linear quadratic regulator (LQR) and learn its parameters using an information-efficient BO algorithm. Such algorithm uses Gaussian processes (GPs) to model the unknown performance objective. The GP model is used by BO to suggest controller parameters that are expected to increment the information about the optimal parameters, measured as a gain in entropy. The resulting “automatic LQR tuning” framework is demonstrated on two robotic platforms: A robot arm balancing an inverted pole and a humanoid robot performing a squatting task. In both cases, an existing controller is automatically improved in a handful of experiments without human intervention. BO compensates for data scarcity by means of the GP, which is a probabilistic model that encodes prior assumptions about the unknown performance objective. Usually, incorrect or non-informed assumptions have negative consequences, such as higher number of robot experiments, poor tuning performance or reduced sample-efficiency. The second to fourth contributions presented herein attempt to alleviate this issue. The second contribution proposes to include the robot simulator into the learning loop as an additional information source for automatic controller tuning. While doing a real robot experiment generally entails high associated costs (e.g., require preparation and take time), simulations are cheaper to obtain (e.g., they can be computed faster). However, because the simulator is an imperfect model of the robot, its information is biased and could have negative repercussions in the learning performance. To address this problem, we propose “simu-vs-real”, a principled multi-fidelity BO algorithm that trades off cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. The resulting algorithm is demonstrated on a cart-pole system, where simulations and real experiments are alternated, thus sparing many real evaluations. The third contribution explores how to adequate the expressiveness of the probabilistic prior to the control problem at hand. To this end, the mathematical structure of LQR controllers is leveraged and embedded into the GP, by means of the kernel function. Specifically, we propose two different “LQR kernel” designs that retain the flexibility of Bayesian nonparametric learning. Simulated results indicate that the LQR kernel yields superior performance than non-informed kernel choices when used for controller learning with BO. Finally, the fourth contribution specifically addresses the problem of handling controller failures, which are typically unavoidable in practice while learning from data, specially if non-conservative solutions are expected. Although controller failures are generally problematic (e.g., the robot has to be emergency-stopped), they are also a rich information source about what should be avoided. We propose “failures-aware excursion search”, a novel algorithm for Bayesian optimization under black-box constraints, where failures are limited in number. Our results in numerical benchmarks indicate that by allowing a confined number of failures, better optima are revealed as compared with state-of-the-art methods. The first contribution of this thesis, “automatic LQR tuning”, lies among the first on applying BO to real robots. While it demonstrated automatic controller learning from few experimental samples, it also revealed several important challenges, such as the need of higher sample-efficiency, which opened relevant research directions that we addressed through several methodological contributions. Summarizing, we proposed “simu-vs-real”, a novel BO algorithm that includes the simulator as an additional information source, an “LQR kernel” design that learns faster than standard choices and “failures-aware excursion search”, a new BO algorithm for constrained black-box optimization problems, where the number of failures is limited.

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Repository (Universitätsbibliothek) - University of Tübingen PDF DOI [BibTex]


Microfibers with mushroom-shaped tips for optimal adhesion
Microfibers with mushroom-shaped tips for optimal adhesion

Sitti, M., Aksak, B.

Google Patents, 2020, US Patent 10,689,549 (misc)

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[BibTex]

[BibTex]


Electronics, Software and Analysis of a Bioinspired Sensorized Quadrupedal Robot
Electronics, Software and Analysis of a Bioinspired Sensorized Quadrupedal Robot

Petereit, R.

Technische Universität München, 2020 (mastersthesis)

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[BibTex]

2019


Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

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PDF [BibTex]

2019


PDF [BibTex]


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Ferromagnetic colloids in liquid crystal solvents

Zarubin, G.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fluctuating interface with a pinning potential

Pranjić, Daniel

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

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[BibTex]

[BibTex]


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Controlling pattern formation in the confined Schnakenberg model

Beyer, David Bernhard

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

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[BibTex]

[BibTex]


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Interfaces in fluids of ionic liquid crystals

Bartsch, H.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Special issue on transport in narrow channels
Journal of Physics: Condensed Matter, 31, IOP Publishing, Bristol, 2019 (misc)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

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[BibTex]

[BibTex]

2017


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Editorial for the Special Issue on Microdevices and Microsystems for Cell Manipulation

Hu, W., Ohta, A. T.

8, Multidisciplinary Digital Publishing Institute, September 2017 (misc)

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DOI [BibTex]

2017


DOI [BibTex]


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Stationary and time-dependent heat transfer in paradigmatic many-body geometries

Asheichyk, Kiryl

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Non-equilibrium forces after temperature quenches in ideal fluids with conserved density

Hölzl, Christian

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Numerical studies of active colloids at fluid interfaces

Peter, Toni

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Collective dynamics of laterally confined active particles near fluid-fluid interfaces

Kistner, Irina

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Self-diffusion of DNA grafted functional colloids in a crowded environment

Werner, M.

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Electrostatic interaction between non-identical charged particles at an electrolyte interface

Schmetzer, Timo

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


Evaluation of the passive dynamics of compliant legs with inertia
Evaluation of the passive dynamics of compliant legs with inertia

Györfi, B.

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

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[BibTex]

[BibTex]


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Microscopic investigation of the Marangoni effect

Pöhnl, Matthias

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]


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Interfacial structure of a catalytic surface

Lipp, Melanie

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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[BibTex]

[BibTex]

2012


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Methods, apparatuses, and systems for micromanipulation with adhesive fibrillar structures

Sitti, M., Mengüç, Y.

December 2012, US Patent App. 14/368,079 (misc)

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[BibTex]

2012



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Dry adhesive structures

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

December 2012, US Patent App. 13/533,386 (misc)

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[BibTex]

[BibTex]


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Methods of making dry adhesives

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

June 2012, US Patent 8,206,631 (misc)

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[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

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

March 2012, US Patent App. 13/429,621 (misc)

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[BibTex]

[BibTex]


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Nanodroplets at topographic steps

Bartsch, H.

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Janus particles in critical liquids

Labbe-Laurent, M.

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Phase equilibria of binary liquid crystals

Klöss, Hans-Christian

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Pinning of drops at superhydrophobic surfaces

Daschke, Lena

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Impedance spectroscopy of ions at interfaces

Reindl, A.

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Surface of an evaporating liquid

Arnold, Daniel

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Statics and dynamics of critical Casimir forces

Tröndle, M.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

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link (url) [BibTex]

link (url) [BibTex]


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Critical Casimir forces beyond the Derjaguin approximation

Brunner, Niklas

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]


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Crystallization of flexible molecules

Held, Felix

Universität Stuttgart, Stuttgart, 2012 (mastersthesis)

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[BibTex]

[BibTex]