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2018


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Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Kajihara, T., Kanagawa, M., Yamazaki, K., Fukumizu, K.

Proceedings of the 35th International Conference on Machine Learning, pages: 2405-2414, PMLR, July 2018 (conference)

Abstract
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.

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Paper [BibTex]

2018


Paper [BibTex]


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Enhanced Non-Steady Gliding Performance of the MultiMo-Bat through Optimal Airfoil Configuration and Control Strategy

Kim, H., Woodward, M. A., Sitti, M.

In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1382-1388, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

Balles, L., Hennig, P.

In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 (inproceedings) Accepted

Abstract
The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an estimate of their relative variance. We disentangle these two aspects and analyze them in isolation, gaining insight into the mechanisms underlying ADAM. This analysis also extends recent results on adverse effects of ADAM on generalization, isolating the sign aspect as the problematic one. Transferring the variance adaptation to SGD gives rise to a novel method, completing the practitioner's toolbox for problems where ADAM fails.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Collectives of Spinning Mobile Microrobots for Navigation and Object Manipulation at the Air-Water Interface

Wang, W., Kishore, V., Koens, L., Lauga, E., Sitti, M.

In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1-9, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H. B., Sari, A. E., Soylu, U., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-7, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Endosensorfusion: Particle filtering-based multi-sensory data fusion with switching state-space model for endoscopic capsule robots

Turan, M., Almalioglu, Y., Gilbert, H., Araujo, H., Cemgil, T., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-8, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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[BibTex]

[BibTex]

2016


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Steering control of a water-running robot using an active tail

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

In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pages: 4945-4950, October 2016 (inproceedings)

Abstract
Many highly dynamic novel mobile robots have been developed being inspired by animals. In this study, we are inspired by a basilisk lizard's ability to run and steer on water surface for a hexapedal robot. The robot has an active tail with a circular plate, which the robot rotates to steer on water. We dynamically modeled the platform and conducted simulations and experiments on steering locomotion with a bang-bang controller. The robot can steer on water by rotating the tail, and the controlled steering locomotion is stable. The dynamic modelling approximates the robot's steering locomotion and the trends of the simulations and experiments are similar, although there are errors between the desired and actual angles. The robot's maneuverability on water can be improved through further research.

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DOI [BibTex]

2016


DOI [BibTex]


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Targeting of cell mockups using sperm-shaped microrobots in vitro

Khalil, I. S., Tabak, A. F., Hosney, A., Klingner, A., Shalaby, M., Abdel-Kader, R. M., Serry, M., Sitti, M.

In Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on, pages: 495-501, July 2016 (inproceedings)

Abstract
Sperm-shaped microrobots are controlled under the influence of weak oscillating magnetic fields (milliTesla range) to selectively target cell mockups (i.e., gas bubbles with average diameter of 200 μm). The sperm-shaped microrobots are fabricated by electrospinning using a solution of polystyrene, dimethylformamide, and iron oxide nanoparticles. These nanoparticles are concentrated within the head of the microrobot, and hence enable directional control along external magnetic fields. The magnetic dipole moment of the microrobot is characterized (using the flip-time technique) to be 1.4×10-11 A.m2, at magnetic field of 28 mT. In addition, the morphology of the microrobot is characterized using Scanning Electron Microscopy images. The characterized parameters and morphology are used in the simulation of the locomotion mechanism of the microrobot to prove that its motion depends on breaking the time-reversal symmetry, rather than pulling with the magnetic field gradient. We experimentally demonstrate that the microrobot can controllably follow S-shaped, U-shaped, and square paths, and selectively target the cell mockups using image guidance and under the influence of the oscillating magnetic fields.

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DOI [BibTex]

DOI [BibTex]


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Analysis of the magnetic torque on a tilted permanent magnet for drug delivery in capsule robots

Munoz, F., Alici, G., Zhou, H., Li, W., Sitti, M.

In Advanced Intelligent Mechatronics (AIM), 2016 IEEE International Conference on, pages: 1386-1391, July 2016 (inproceedings)

Abstract
In this paper, we present the analysis of the torque transmitted to a tilted permanent magnet that is to be embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using an analytical model and experimental results for a small cubic permanent magnet that is driven by an external magnetic system made of an array of arc-shaped permanent magnets (ASMs). Our experimental results, which are in agreement with the analytical results, show that the cubic permanent magnet can safely be actuated for inclinations lower than 75° without having to make positional adjustments in the external magnetic system. We have found that with further inclinations, the cubic permanent magnet to be embedded in a drug delivery mechanism may stall. When it stalls, the external magnetic system's position and orientation would have to be adjusted to actuate the cubic permanent magnet and the drug release mechanism. This analysis of the transmitted torque is helpful for the development of real-time control strategies for magnetically articulated devices.

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DOI [BibTex]

DOI [BibTex]


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Active Uncertainty Calibration in Bayesian ODE Solvers

Kersting, H., Hennig, P.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), pages: 309-318, (Editors: Ihler, A. and Janzing, D.), AUAI Press, June 2016 (conference)

Abstract
There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et al.~introduced a sampling-based class of methods that are `well-calibrated' in a specific sense. But the computational cost of these methods is significantly above that of classic methods. On the other hand, Schober et al.~pointed out a precise connection between classic Runge-Kutta ODE solvers and Gaussian filters, which gives only a rough probabilistic calibration, but at negligible cost overhead. By formulating the solution of ODEs as approximate inference in linear Gaussian SDEs, we investigate a range of probabilistic ODE solvers, that bridge the trade-off between computational cost and probabilistic calibration, and identify the inaccurate gradient measurement as the crucial source of uncertainty. We propose the novel filtering-based method Bayesian Quadrature filtering (BQF) which uses Bayesian quadrature to actively learn the imprecision in the gradient measurement by collecting multiple gradient evaluations.

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link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


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Automatic LQR Tuning Based on Gaussian Process Global Optimization

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree- of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four- dimensional tuning problems highlight the method’s potential for automatic controller tuning on robotic platforms.

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Video PDF DOI Project Page [BibTex]

Video PDF DOI Project Page [BibTex]


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Sperm-shaped magnetic microrobots: Fabrication using electrospinning, modeling, and characterization

Khalil, I. S., Tabak, A. F., Hosney, A., Mohamed, A., Klingner, A., Ghoneima, M., Sitti, M.

In Robotics and Automation (ICRA), 2016 IEEE International Conference on, pages: 1939-1944, May 2016 (inproceedings)

Abstract
We use electrospinning to fabricate sperm-shaped magnetic microrobots with a range of diameters from 50 μm to 500 μm. The variables of the electrospinning operation (voltage, concentration of the solution, dynamic viscosity, and distance between the syringe needle and collector) to achieve beading effect are determined. This beading effect allows us to fabricate microrobots with similar morphology to that of sperm cells. The bead and the ultra-fine fiber resemble the morphology of the head and tail of the sperm cell, respectively. We incorporate iron oxide nanoparticles to the head of the sperm-shaped microrobot to provide a magnetic dipole moment. This dipole enables directional control under the influence of external magnetic fields. We also apply weak (less than 2 mT) oscillating magnetic fields to exert a magnetic torque on the magnetic head, and generate planar flagellar waves and flagellated swim. The average speed of the sperm-shaped microrobot is calculated to be 0.5 body lengths per second and 1 body lengths per second at frequencies of 5 Hz and 10 Hz, respectively. We also develop a model of the microrobot using elastohydrodynamics approach and Timoshenko-Rayleigh beam theory, and find good agreement with the experimental results.

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DOI [BibTex]

DOI [BibTex]


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Batch Bayesian Optimization via Local Penalization

González, J., Dai, Z., Hennig, P., Lawrence, N.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51, pages: 648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), May 2016 (conference)

ei pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Probabilistic Approximate Least-Squares

Bartels, S., Hennig, P.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), May 2016 (conference)

Abstract
Least-squares and kernel-ridge / Gaussian process regression are among the foundational algorithms of statistics and machine learning. Famously, the worst-case cost of exact nonparametric regression grows cubically with the data-set size; but a growing number of approximations have been developed that estimate good solutions at lower cost. These algorithms typically return point estimators, without measures of uncertainty. Leveraging recent results casting elementary linear algebra operations as probabilistic inference, we propose a new approximate method for nonparametric least-squares that affords a probabilistic uncertainty estimate over the error between the approximate and exact least-squares solution (this is not the same as the posterior variance of the associated Gaussian process regressor). This allows estimating the error of the least-squares solution on a subset of the data relative to the full-data solution. The uncertainty can be used to control the computational effort invested in the approximation. Our algorithm has linear cost in the data-set size, and a simple formal form, so that it can be implemented with a few lines of code in programming languages with linear algebra functionality.

ei pn

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]

2012


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Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Abstract
Four decades after their invention, quasi- Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

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website+code pdf link (url) [BibTex]

2012


website+code pdf link (url) [BibTex]


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Learning Tracking Control with Forward Models

Bócsi, B., Hennig, P., Csató, L., Peters, J.

In pages: 259 -264, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract
Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

ei pn

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

ei pn

Web [BibTex]

Web [BibTex]


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Kernel Topic Models

Hennig, P., Stern, D., Herbrich, R., Graepel, T.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

ei pn

PDF Web [BibTex]

PDF Web [BibTex]


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Topological optimization for continuum compliant mechanisms via morphological evolution of traditional mechanisms

Lum, GZ, Yeo, SH, Yang, GL, Teo, TJ, Sitti, M

In 4th International Conference on Computational Methods, pages: 8, 2012 (inproceedings)

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[BibTex]

[BibTex]


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Flapping Wings with DC-Motors via Direct, Elastic Transmissions

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

In Proceedings of International Conference on Intelligent Unmanned Systems, 8, 2012 (inproceedings)

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[BibTex]

[BibTex]


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Investigation of bioinspired gecko fibers to improve adhesion of HeartLander surgical robot

Tortora, G., Glass, P., Wood, N., Aksak, B., Menciassi, A., Sitti, M., Riviere, C.

In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pages: 908-911, 2012 (inproceedings)

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[BibTex]

[BibTex]


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Magnetic hysteresis for multi-state addressable magnetic microrobotic control

Diller, E., Miyashita, S., Sitti, M.

In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages: 2325-2331, 2012 (inproceedings)

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[BibTex]

[BibTex]

2008


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Simulation and analysis of a passive pitch reversal flapping wing mechanism for an aerial robotic platform

Arabagi, V., Sitti, M.

In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages: 1260-1265, 2008 (inproceedings)

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Project Page [BibTex]

2008


Project Page [BibTex]


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Fabrication and Characterization of Biologically Inspired Mushroom-Shaped Elastomer Microfiber Arrays

Kim, S., Sitti, M.

In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pages: 839-847, 2008 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Gecko inspired micro-fibrillar adhesives for wall climbing robots on micro/nanoscale rough surfaces

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

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 3058-3063, 2008 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Miniature Mobile Robots Down to Micron Scale

Sitti, M.

In Micro-NanoMechatronics and Human Science, 2008. MHS 2008. International Symposium on, pages: 525-525, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Polymeric Micro/Nanofiber Manufacturing and Mechanical Characterization

Nain, A. S., Sitti, M., Amon, C.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 295-303, 2008 (inproceedings)

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[BibTex]

[BibTex]


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An untethered magnetically actuated micro-robot capable of motion on arbitrary surfaces

Floyd, S., Pawashe, C., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 419-424, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Fabrication of bio-inspired elastomer nanofiber arrays with spatulate tips using notching effect

Kim, S., Sitti, M., Jang, J., Thomas, E. L.

In Nanotechnology, 2008. NANO’08. 8th IEEE Conference on, pages: 780-782, 2008 (inproceedings)

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[BibTex]

[BibTex]


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A motorized anchoring mechanism for a tethered capsule robot using fibrillar adhesives for interventions in the esophagus

Glass, P., Cheung, E., Wang, H., Appasamy, R., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, pages: 758-764, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Fabrication of Single and Multi-Layer Fibrous Biomaterial Scaffolds for Tissue Engineering

Nain, A. S., Miller, E., Sitti, M., Campbell, P., Amon, C.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 231-238, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Performance of different foot designs for a water running robot

Floyd, S., Adilak, S., Ramirez, S., Rogman, R., Sitti, M.

In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pages: 244-250, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Dynamic modeling of a basilisk lizard inspired quadruped robot running on water

Park, H. S., Floyd, S., Sitti, M.

In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages: 3101-3107, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Bacterial propulsion of chemically patterned micro-cylinders

Behkam, B., Sitti, M.

In Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, pages: 753-757, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Design and Numerical Modeling of an On-Board Chemical Release Module for Motion Control of Bacteria-Propelled Swimming Micro-Robots

Behkam, B., Nain, A. S., Amon, C. H., Sitti, M.

In ASME 2008 International Mechanical Engineering Congress and Exposition, pages: 239-244, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Investigation of Calcium Mechanotransduction by Quasi 3-D Microfiber Mechanical Stimulation of Cells

Ruder, W. C., Pratt, E. D., Sitti, M., LeDuc, P. R., Antaki, J. F.

In ASME 2008 Summer Bioengineering Conference, pages: 1049-1050, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Beanbag robotics: Robotic swarms with 1-dof units

Kriesel, D. M., Cheung, E., Sitti, M., Lipson, H.

In International Conference on Ant Colony Optimization and Swarm Intelligence, pages: 267-274, 2008 (inproceedings)

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[BibTex]

[BibTex]


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Particle image velocimetry and thrust of flagellar micro propulsion systems

Danis, U., Sitti, M., Pekkan, K.

In APS Division of Fluid Dynamics Meeting Abstracts, 1, 2008 (inproceedings)

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[BibTex]

[BibTex]