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2019


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A Learnable Safety Measure

Heim, S., Rohr, A. V., Trimpe, S., Badri-Spröwitz, A.

Conference on Robot Learning, November 2019 (conference) Accepted

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Arxiv [BibTex]

2019


Arxiv [BibTex]


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Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots

Drama, Ö., Badri-Spröwitz, A.

Proceedings International Conference on Humanoid Robots, Humanoids, September 2019 (conference) Accepted

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link (url) [BibTex]

link (url) [BibTex]


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Series Elastic Behavior of Biarticular Muscle-Tendon Structure in a Robotic Leg

Ruppert, F., Badri-Spröwitz, A.

Frontiers in Neurorobotics, 64, pages: 13, 13, August 2019 (article)

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Frontiers YouTube link (url) DOI [BibTex]

Frontiers YouTube link (url) DOI [BibTex]


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The positive side of damping

Heim, S., Millard, M., Le Mouel, C., Sproewitz, A.

Proceedings of AMAM, The 9th International Symposium on Adaptive Motion of Animals and Machines, August 2019 (conference) Accepted

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[BibTex]

[BibTex]


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Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics

Steve Heim, , Spröwitz, A.

IEEE Transactions on Robotics (T-RO) , 35(4), pages: 939-952, August 2019 (article)

Abstract
Properly designing a system to exhibit favorable natural dynamics can greatly simplify designing or learning the control policy. However, it is still unclear what constitutes favorable natural dynamics and how to quantify its effect. Most studies of simple walking and running models have focused on the basins of attraction of passive limit cycles and the notion of self-stability. We instead emphasize the importance of stepping beyond basins of attraction. In this paper, we show an approach based on viability theory to quantify robust sets in state-action space. These sets are valid for the family of all robust control policies, which allows us to quantify the robustness inherent to the natural dynamics before designing the control policy or specifying a control objective. We illustrate our formulation using spring-mass models, simple low-dimensional models of running systems. We then show an example application by optimizing robustness of a simulated planar monoped, using a gradient-free optimization scheme. Both case studies result in a nonlinear effective stiffness providing more robustness.

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arXiv preprint arXiv:1806.08081 T-RO link (url) DOI Project Page [BibTex]

arXiv preprint arXiv:1806.08081 T-RO link (url) DOI Project Page [BibTex]


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DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Elastic modulus affects adhesive strength of gecko-inspired synthetics in variable temperature and humidity

Mitchell, CT, Drotlef, D, Dayan, CB, Sitti, M, Stark, AY

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E372-E372, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, March 2019 (inproceedings)

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[BibTex]

[BibTex]


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X-ray Optics Fabrication Using Unorthodox Approaches

Sanli, U., Baluktsian, M., Ceylan, H., Sitti, M., Weigand, M., Schütz, G., Keskinbora, K.

Bulletin of the American Physical Society, APS, 2019 (article)

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[BibTex]

[BibTex]


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Microrobotics and Microorganisms: Biohybrid Autonomous Cellular Robots

Alapan, Y., Yasa, O., Yigit, B., Yasa, I. C., Erkoc, P., Sitti, M.

Annual Review of Control, Robotics, and Autonomous Systems, 2019 (article)

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[BibTex]

[BibTex]


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Tailored Magnetic Springs for Shape-Memory Alloy Actuated Mechanisms in Miniature Robots

Woodward, M. A., Sitti, M.

IEEE Transactions on Robotics, 35, 2019 (article)

Abstract
Animals can incorporate large numbers of actuators because of the characteristics of muscles; whereas, robots cannot, as typical motors tend to be large, heavy, and inefficient. However, shape-memory alloys (SMA), materials that contract during heating because of change in their crystal structure, provide another option. SMA, though, is unidirectional and therefore requires an additional force to reset (extend) the actuator, which is typically provided by springs or antagonistic actuation. These strategies, however, tend to limit the actuator's work output and functionality as their force-displacement relationships typically produce increasing resistive force with limited variability. In contrast, magnetic springs-composed of permanent magnets, where the interaction force between magnets mimics a spring force-have much more variable force-displacement relationships and scale well with SMA. However, as of yet, no method for designing magnetic springs for SMA-actuators has been demonstrated. Therefore, in this paper, we present a new methodology to tailor magnetic springs to the characteristics of these actuators, with experimental results both for the device and robot-integrated SMA-actuators. We found magnetic building blocks, based on sets of permanent magnets, which are well-suited to SMAs and have the potential to incorporate features such as holding force, state transitioning, friction minimization, auto-alignment, and self-mounting. We show magnetic springs that vary by more than 3 N in 750 $\mu$m and two SMA-actuated devices that allow the MultiMo-Bat to reach heights of up to 4.5 m without, and 3.6 m with, integrated gliding airfoils. Our results demonstrate the potential of this methodology to add previously impossible functionality to smart material actuators. We anticipate this methodology will inspire broader consideration of the use of magnetic springs in miniature robots and further study of the potential of tailored magnetic springs throughout mechanical systems.

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DOI [BibTex]


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Magnetically Actuated Soft Capsule Endoscope for Fine-Needle Biopsy

Son, D., Gilbert, H., Sitti, M.

Soft robotics, Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New …, 2019 (article)

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[BibTex]

[BibTex]


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Thrust and Hydrodynamic Efficiency of the Bundled Flagella

Danis, U., Rasooli, R., Chen, C., Dur, O., Sitti, M., Pekkan, K.

Micromachines, 10, 2019 (article)

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[BibTex]

[BibTex]


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The near and far of a pair of magnetic capillary disks

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

Soft Matter, 2019 (article)

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[BibTex]

[BibTex]


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Multifarious Transit Gates for Programmable Delivery of Bio‐functionalized Matters

Hu, X., Torati, S. R., Kim, H., Yoon, J., Lim, B., Kim, K., Sitti, M., Kim, C.

Small, Wiley Online Library, 2019 (article)

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[BibTex]

[BibTex]


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Multi-functional soft-bodied jellyfish-like swimming

Ren, Z., Hu, W., Dong, X., Sitti, M.

Nature communications, 10, 2019 (article)

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[BibTex]


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Welcome to Progress in Biomedical Engineering

Sitti, M.

Progress in Biomedical Engineering, 1, IOP Publishing, 2019 (article)

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[BibTex]

[BibTex]


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Quantifying the Robustness of Natural Dynamics: a Viability Approach

Heim, S., Sproewitz, A.

Proceedings of Dynamic Walking , Dynamic Walking , 2019 (conference) Accepted

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Submission DW2019 [BibTex]

Submission DW2019 [BibTex]


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Mechanics of a pressure-controlled adhesive membrane for soft robotic gripping on curved surfaces

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

Extreme Mechanics Letters, Elsevier, 2019 (article)

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[BibTex]


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Graphene oxide synergistically enhances antibiotic efficacy in Vancomycin resistance Staphylococcus aureus

Singh, V., Kumar, V., Kashyap, S., Singh, A. V., Kishore, V., Sitti, M., Saxena, P. S., Srivastava, A.

ACS Applied Bio Materials, ACS Publications, 2019 (article)

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[BibTex]

[BibTex]


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Review of emerging concepts in nanotoxicology: opportunities and challenges for safer nanomaterial design

Singh, A. V., Laux, P., Luch, A., Sudrik, C., Wiehr, S., Wild, A., Santamauro, G., Bill, J., Sitti, M.

Toxicology Mechanisms and Methods, 2019 (article)

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[BibTex]

[BibTex]


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Multifunctional and biodegradable self-propelled protein motors

Pena-Francesch, A., Giltinan, J., Sitti, M.

Nature communications, 10, Nature Publishing Group, 2019 (article)

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[BibTex]

[BibTex]


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Cohesive self-organization of mobile microrobotic swarms

Yigit, B., Alapan, Y., Sitti, M.

arXiv preprint arXiv:1907.05856, 2019 (article)

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[BibTex]

[BibTex]


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Mobile microrobots for active therapeutic delivery

Erkoc, P., Yasa, I. C., Ceylan, H., Yasa, O., Alapan, Y., Sitti, M.

Advanced Therapeutics, Wiley Online Library, 2019 (article)

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[BibTex]

[BibTex]


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Shape-encoded dynamic assembly of mobile micromachines

Alapan, Y., Yigit, B., Beker, O., Demirörs, A. F., Sitti, M.

Nature, 18, 2019 (article)

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[BibTex]

[BibTex]


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Microfluidics Integrated Lithography‐Free Nanophotonic Biosensor for the Detection of Small Molecules

Sreekanth, K. V., Sreejith, S., Alapan, Y., Sitti, M., Lim, C. T., Singh, R.

Advanced Optical Materials, 2019 (article)

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[BibTex]

[BibTex]


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Gecko-inspired composite microfibers for reversible adhesion on smooth and rough surfaces

Drotlef, D., Dayan, C., Sitti, M.

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E58-E58, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, 2019 (inproceedings)

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[BibTex]

[BibTex]


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ENGINEERING Bio-inspired robotic collectives

Sitti, M.

Nature, 567, pages: 314-315, Macmillan Publishers Ltd., London, England, 2019 (article)

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[BibTex]

[BibTex]


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Peptide-Induced Biomineralization of Tin Oxide (SnO2) Nanoparticles for Antibacterial Applications

Singh, A. V., Jahnke, T., Xiao, Y., Wang, S., Yu, Y., David, H., Richter, G., Laux, P., Luch, A., Srivastava, A., Saxena, P. S., Bill, J., Sitti, M.

Journal of nanoscience and nanotechnology, 19, American Scientific Publishers, 2019 (article)

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[BibTex]

[BibTex]


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Electromechanical actuation of dielectric liquid crystal elastomers for soft robotics

Davidson, Z., Shahsavan, H., Guo, Y., Hines, L., Xia, Y., Yang, S., Sitti, M.

Bulletin of the American Physical Society, APS, 2019 (article)

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[BibTex]

[BibTex]


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Probabilistic Linear Solvers: A Unifying View

Bartels, S., Cockayne, J., Ipsen, I. C. F., Hennig, P.

Statistics and Computing, 2019 (article) Accepted

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link (url) [BibTex]

link (url) [BibTex]


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Learning to Navigate Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H. B., Mahmood, F., Durr, N. J., Araujo, H., Sarı, A. E., Ajay, A., Sitti, M.

IEEE Robotics and Automation Letters, 4, 2019 (article)

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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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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]

website+code pdf link (url) [BibTex]


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Entropy Search for Information-Efficient Global Optimization

Hennig, P., Schuler, C.

Journal of Machine Learning Research, 13, pages: 1809-1837, -, June 2012 (article)

Abstract
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.

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PDF Web Project Page [BibTex]

PDF Web Project Page [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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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.

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]