Header logo is


2020


Thumb xl hetl
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks

Beuchert, J., Solowjow, F., Raisch, J., Trimpe, S., Seel, T.

IEEE Control Systems Letters, 4(1):103-108, January 2020 (article) To be published

ics

arXiv PDF DOI [BibTex]

2020


arXiv PDF DOI [BibTex]


Thumb xl l css
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

Baumann, D., Mager, F., Zimmerling, M., Trimpe, S.

IEEE Control Systems Letters, 4(1):127-132, January 2020 (article) To be published

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]

2019


Thumb xl fire
Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources

Haksar, R., Solowjow, F., Trimpe, S., Schwager, M.

In Proceedings of the 58th IEEE International Conference on Decision and Control (CDC) , 58th IEEE International Conference on Decision and Control (CDC), December 2019 (proceedings) Accepted

ics

PDF [BibTex]

2019


PDF [BibTex]


Thumb xl screenshot 2019 09 12 at 07.43.13
A Learnable Safety Measure

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

Conference on Robot Learning, November 2019 (conference) Accepted

dlg ics

[BibTex]

[BibTex]


Thumb xl distributed pt control
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems

Mastrangelo, J. M., Baumann, D., Trimpe, S.

In Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), September 2019 (inproceedings) Accepted

ics

arXiv PDF [BibTex]

arXiv PDF [BibTex]


Thumb xl acc pulse ctrl
Event-triggered Pulse Control with Model Learning (if Necessary)

Baumann, D., Solowjow, F., Johansson, K. H., Trimpe, S.

In Proceedings of the American Control Conference, pages: 792-797, American Control Conference (ACC), July 2019 (inproceedings)

ics

arXiv PDF [BibTex]

arXiv PDF [BibTex]


Thumb xl pic
Data-driven inference of passivity properties via Gaussian process optimization

Romer, A., Trimpe, S., Allgöwer, F.

In Proceedings of the European Control Conference, European Control Conference (ECC), June 2019 (inproceedings) Accepted

ics

PDF [BibTex]

PDF [BibTex]


Thumb xl coverimage1
Trajectory-Based Off-Policy Deep Reinforcement Learning

Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., Daniel, C.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), June 2019 (inproceedings) Accepted

Abstract
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods.

ics

arXiv PDF [BibTex]

arXiv PDF [BibTex]


Thumb xl trimpe2019resource image
Resource-aware IoT Control: Saving Communication through Predictive Triggering

Trimpe, S., Baumann, D.

IEEE Internet of Things Journal, 6(3):5013-5028, June 2019 (article)

Abstract
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.

ics

PDF arXiv DOI [BibTex]

PDF arXiv DOI [BibTex]


Thumb xl testbed v5
Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

(Best Paper Award)

Mager, F., Baumann, D., Jacob, R., Thiele, L., Trimpe, S., Zimmerling, M.

In Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, pages: 97-108, 10th ACM/IEEE International Conference on Cyber-Physical Systems, April 2019 (inproceedings)

Abstract
Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals below 100 ms. Low-power wireless is preferred for its flexibility, low cost, and small form factor, especially if the devices support multi-hop communication. Thus far, however, closed-loop control over multi-hop low-power wireless has only been demonstrated for update intervals on the order of multiple seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance such as jitter or packet loss, and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for linear dynamic systems. Using experiments on a testbed with multiple cart-pole systems, we are the first to demonstrate the feasibility and to assess the performance of closed-loop control and coordination over multi-hop low-power wireless for update intervals from 20 ms to 50 ms.

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


no image
Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems

(Best Demo Award)

Mager, F., Baumann, D., Jacob, R., Thiele, L., Trimpe, S., Zimmerling, M.

Proceedings of the 18th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), pages: 340-341, 18th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), April 2019 (poster)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Thumb xl lic overview
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

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

ics

PDF [BibTex]

PDF [BibTex]


Thumb xl journal iav
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

Neumann-Brosig, M., Marco, A., Schwarzmann, D., Trimpe, S.

IEEE Transactions on Control Systems Technology, 2019 (article) Accepted

Abstract
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.

ics

arXiv (PDF) DOI Project Page [BibTex]

arXiv (PDF) DOI Project Page [BibTex]


no image
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)

pi

[BibTex]

[BibTex]


no image
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)

mms pi

[BibTex]

[BibTex]


no image
The near and far of a pair of magnetic capillary disks

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

Soft Matter, 2019 (article)

pi

[BibTex]

[BibTex]


no image
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)

pi

[BibTex]

[BibTex]


no image
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)

pi

[BibTex]

[BibTex]


Thumb xl mode changes long exp
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Baumann, D., Mager, F., Jacob, R., Thiele, L., Zimmerling, M., Trimpe, S.

ACM Transactions on Cyber-Physical Systems, 2019 (article) Accepted

ics

[BibTex]

[BibTex]


no image
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)

pi

[BibTex]

[BibTex]


no image
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)

pi

[BibTex]

[BibTex]


Thumb xl blockdiag
Event-triggered Learning

Solowjow, F., Trimpe, S.

2019 (techreport) Submitted

ics

arXiv PDF [BibTex]


no image
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)

pi

[BibTex]

[BibTex]

2018


Thumb xl screenshot 2018 5 9 swimming back and forth using planar flagellar propulsion at low reynolds numbers   khalil   2018   adv ...
Swimming Back and Forth Using Planar Flagellar Propulsion at Low Reynolds Numbers

Khalil, I. S. M., Tabak, A. F., Hamed, Y., Mitwally, M. E., Tawakol, M., Klingner, A., Sitti, M.

Advanced Science, 5(2):1700461, 2018 (article)

Abstract
Abstract Peritrichously flagellated Escherichia coli swim back and forth by wrapping their flagella together in a helical bundle. However, other monotrichous bacteria cannot swim back and forth with a single flagellum and planar wave propagation. Quantifying this observation, a magnetically driven soft two‐tailed microrobot capable of reversing its swimming direction without making a U‐turn trajectory or actively modifying the direction of wave propagation is designed and developed. The microrobot contains magnetic microparticles within the polymer matrix of its head and consists of two collinear, unequal, and opposite ultrathin tails. It is driven and steered using a uniform magnetic field along the direction of motion with a sinusoidally varying orthogonal component. Distinct reversal frequencies that enable selective and independent excitation of the first or the second tail of the microrobot based on their tail length ratio are found. While the first tail provides a propulsive force below one of the reversal frequencies, the second is almost passive, and the net propulsive force achieves flagellated motion along one direction. On the other hand, the second tail achieves flagellated propulsion along the opposite direction above the reversal frequency.

pi

link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


Thumb xl learn etc
Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

al ics

arXiv PDF DOI Project Page Project Page [BibTex]

arXiv PDF DOI Project Page Project Page [BibTex]


Thumb xl unbenannte pr%c3%a4sentation 1
Efficient Encoding of Dynamical Systems through Local Approximations

Solowjow, F., Mehrjou, A., Schölkopf, B., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 6073 - 6079 , Miami, Fl, USA, December 2018 (inproceedings)

ei ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Thumb xl lars2018
Depth Control of Underwater Robots using Sliding Modes and Gaussian Process Regression

Lima, G. S., Bessa, W. M., Trimpe, S.

In Proceeding of the 15th Latin American Robotics Symposium, João Pessoa, Brazil, 15th Latin American Robotics Symposium, November 2018 (inproceedings)

Abstract
The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation for hydrodynamic effects. In this work, a new robust control scheme is presented for remotely operated underwater vehicles. In order to meet both robustness and tracking requirements, sliding mode control is combined with Gaussian process regression. The convergence properties of the closed-loop signals are analytically proven. Numerical results confirm the stronger improved performance of the proposed control scheme.

ics

[BibTex]

[BibTex]


Thumb xl universal custom complex magnetic spring design methodology
Universal Custom Complex Magnetic Spring Design Methodology

Woodward, M. A., Sitti, M.

IEEE Transactions on Magnetics, 54(1):1-13, October 2018 (article)

Abstract
A design methodology is presented for creating custom complex magnetic springs through the design of force-displacement curves. This methodology results in a magnet configuration, which will produce a desired force-displacement relationship. Initially, the problem is formulated and solved as a system of linear equations. Then, given the limited likelihood of a single solution being feasibly manufactured, key parameters of the solution are extracted and varied to create a family of solutions. Finally, these solutions are refined using numerical optimization. Given the properties of magnets, this methodology can create any well-defined function of force versus displacement and is model-independent. To demonstrate this flexibility, a number of example magnetic springs are designed; one of which, designed for use in a jumping-gliding robot's shape memory alloy actuated clutch, is manufactured and experimentally characterized. Due to the scaling of magnetic forces, the displacement region which these magnetic springs are most applicable is that of millimeters and below. However, this region is well situated for miniature robots and smart material actuators, where a tailored magnetic spring, designed to compliment a component, can enhance its performance while adding new functionality. The methodology is also expendable to variable interactions and multi-dimensional magnetic field design.

pi

DOI [BibTex]

DOI [BibTex]


Thumb xl toc image
Gait learning for soft microrobots controlled by light fields

Rohr, A. V., Trimpe, S., Marco, A., Fischer, P., Palagi, S.

In International Conference on Intelligent Robots and Systems (IROS) 2018, pages: 6199-6206, International Conference on Intelligent Robots and Systems 2018, October 2018 (inproceedings)

Abstract
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing environments. However, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is highly data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semisynthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot’s locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on lightcontrolled soft microrobots and probabilistic learning control.

ics pf

arXiv IEEE Xplore DOI Project Page [BibTex]

arXiv IEEE Xplore DOI Project Page [BibTex]


Thumb xl grasping
Leveraging Contact Forces for Learning to Grasp

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two- fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

am mg

video arXiv [BibTex]

video arXiv [BibTex]


no image
Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty

Soloperto, R., Müller, M. A., Trimpe, S., Allgöwer, F.

In Proceedings of the IFAC Conference on Nonlinear Model Predictive Control (NMPC), Madison, Wisconsin, USA, 6th IFAC Conference on Nonlinear Model Predictive Control, August 2018 (inproceedings)

ics

PDF [BibTex]

PDF [BibTex]


Thumb xl teaser image
Programmable collective behavior in dynamically self-assembled mobile microrobotic swarms

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

Advanced Science, July 2018 (article)

Abstract
Collective control of mobile microrobotic swarms is indispensable for their potential high-impact applications in targeted drug delivery, medical diagnostics, parallel micromanipulation, and environmental sensing and remediation. Lack of on-board computational and sensing capabilities in current microrobotic systems necessitates use of physical interactions among individual microrobots for local physical communication and cooperation. Here, we show that mobile microrobotic swarms with well-defined collective behavior can be designed by engineering magnetic interactions among individual units. Microrobots, consisting of a linear chain of self-assembled magnetic microparticles, locomote on surfaces in response to a precessing magnetic field. Control over the direction of precessing magnetic field allows engineering attractive and repulsive interactions among microrobots and, thus, collective order with well-defined spatial organization and parallel operation over macroscale distances (~ 1 cm). These microrobotic swarms can be guided through confined spaces, while preserving microrobot morphology and function. These swarms can further achieve directional transport of large cargoes on surfaces and small cargoes in bulk fluids. Described design approach, exploiting physical interactions among individual robots, enables facile and rapid formation of self-organized and reconfigurable microrobotic swarms with programmable collective order.

pi

link (url) [BibTex]


Thumb xl picture1
3D-Printed Biodegradable Microswimmer for Drug Delivery and Targeted Cell Labeling

Hakan Ceylan, , I. Ceren Yasa, , Oncay Yasa, , Ahmet Fatih Tabak, , Joshua Giltinan, , Sitti, M.

bioRxiv, pages: 379024, July 2018 (article)

Abstract
Miniaturization of interventional medical devices can leverage minimally invasive technologies by enabling operational resolution at cellular length scales with high precision and repeatability. Untethered micron-scale mobile robots can realize this by navigating and performing in hard-to-reach, confined and delicate inner body sites. However, such a complex task requires an integrated design and engineering strategy, where powering, control, environmental sensing, medical functionality and biodegradability need to be considered altogether. The present study reports a hydrogel-based, biodegradable microrobotic swimmer, which is responsive to the changes in its microenvironment for theranostic cargo delivery and release tasks. We design a double-helical magnetic microswimmer of 20 micrometers length, which is 3D-printed with complex geometrical and compositional features. At normal physiological concentrations, matrix metalloproteinase-2 (MMP-2) enzyme can entirely degrade the microswimmer body in 118 h to solubilized non-toxic products. The microswimmer can respond to the pathological concentrations of MMP-2 by swelling and thereby accelerating the release kinetics of the drug payload. Anti-ErbB 2 antibody-tagged magnetic nanoparticles released from the degraded microswimmers serve for targeted labeling of SKBR3 breast cancer cells to realize the potential of medical imaging of local tissue sites following the therapeutic intervention. These results represent a leap forward toward clinical medical microrobots that are capable of sensing, responding to the local pathological information, and performing specific therapeutic and diagnostic tasks as orderly executed operations using their smart composite material architectures.

pi

DOI Project Page [BibTex]


no image
Learning an Approximate Model Predictive Controller with Guarantees

Hertneck, M., Koehler, J., Trimpe, S., Allgöwer, F.

IEEE Control Systems Letters, 2(3):543-548, July 2018 (article)

Abstract
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Thumb xl mazen
Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

mg ps

pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


Thumb xl teaser image
Probabilistic Recurrent State-Space Models

Doerr, A., Daniel, C., Schiegg, M., Nguyen-Tuong, D., Schaal, S., Toussaint, M., Trimpe, S.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), July 2018 (inproceedings)

Abstract
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex time-series data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalable initialization and training algorithm based on doubly stochastic variational inference and Gaussian processes. In the variational approximation we propose in contrast to related approaches to fully capture the latent state temporal correlations to allow for robust training.

am ics

arXiv pdf Project Page [BibTex]

arXiv pdf Project Page [BibTex]


Thumb xl screen shot 2018 06 29 at 4.24.39 pm
Innate turning preference of leaf-cutting ants in the absence of external orientation cues

Endlein, T., Sitti, M.

Journal of Experimental Biology, The Company of Biologists Ltd, June 2018 (article)

Abstract
Many ants use a combination of cues for orientation but how do ants find their way when all external cues are suppressed? Do they walk in a random way or are their movements spatially oriented? Here we show for the first time that leaf-cutting ants (Acromyrmex lundii) have an innate preference of turning counter-clockwise (left) when external cues are precluded. We demonstrated this by allowing individual ants to run freely on the water surface of a newly-developed treadmill. The surface tension supported medium-sized workers but effectively prevented ants from reaching the wall of the vessel, important to avoid wall-following behaviour (thigmotaxis). Most ants ran for minutes on the spot but also slowly turned counter-clockwise in the absence of visual cues. Reconstructing the effectively walked path revealed a looping pattern which could be interpreted as a search strategy. A similar turning bias was shown for groups of ants in a symmetrical Y-maze where twice as many ants chose the left branch in the absence of optical cues. Wall-following behaviour was tested by inserting a coiled tube before the Y-fork. When ants traversed a left-coiled tube, more ants chose the left box and vice versa. Adding visual cues in form of vertical black strips either outside the treadmill or on one branch of the Y-maze led to oriented walks towards the strips. It is suggested that both, the turning bias and the wall-following are employed as search strategies for an unknown environment which can be overridden by visual cues.

pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl 1
Motility and chemotaxis of bacteria-driven microswimmers fabricated using antigen 43-mediated biotin display

Schauer, O., Mostaghaci, B., Colin, R., Hürtgen, D., Kraus, D., Sitti, M., Sourjik, V.

Scientific Reports, 8(1):9801, Nature Publishing Group, June 2018 (article)

Abstract
Bacteria-driven biohybrid microswimmers (bacteriabots) combine synthetic cargo with motile living bacteria that enable propulsion and steering. Although fabrication and potential use of such bacteriabots have attracted much attention, existing methods of fabrication require an extensive sample preparation that can drastically decrease the viability and motility of bacteria. Moreover, chemotactic behavior of bacteriabots in a liquid medium with chemical gradients has remained largely unclear. To overcome these shortcomings, we designed Escherichia coli to autonomously display biotin on its cell surface via the engineered autotransporter antigen 43 and thus to bind streptavidin-coated cargo. We show that the cargo attachment to these bacteria is greatly enhanced by motility and occurs predominantly at the cell poles, which is greatly beneficial for the fabrication of motile bacteriabots. We further performed a systemic study to understand and optimize the ability of these bacteriabots to follow chemical gradients. We demonstrate that the chemotaxis of bacteriabots is primarily limited by the cargo-dependent reduction of swimming speed and show that the fabrication of bacteriabots using elongated E. coli cells can be used to overcome this limitation.

pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl unbenannte pr%c3%a4sentation
Event-triggered Learning for Resource-efficient Networked Control

Solowjow, F., Baumann, D., Garcke, J., Trimpe, S.

In Proceedings of the American Control Conference (ACC), pages: 6506 - 6512, American Control Conference, June 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Thumb xl 41586 2018 250 fig1 html
Multifunctional ferrofluid-infused surfaces with reconfigurable multiscale topography

Wang, W., Timonen, J. V. I., Carlson, A., Drotlef, D., Zhang, C. T., Kolle, S., Grinthal, A., Wong, T., Hatton, B., Kang, S. H., Kennedy, S., Chi, J., Blough, R. T., Sitti, M., Mahadevan, L., Aizenberg, J.

Nature, June 2018 (article)

Abstract
Developing adaptive materials with geometries that change in response to external stimuli provides fundamental insights into the links between the physical forces involved and the resultant morphologies and creates a foundation for technologically relevant dynamic systems1,2. In particular, reconfigurable surface topography as a means to control interfacial properties 3 has recently been explored using responsive gels 4 , shape-memory polymers 5 , liquid crystals6-8 and hybrid composites9-14, including magnetically active slippery surfaces12-14. However, these designs exhibit a limited range of topographical changes and thus a restricted scope of function. Here we introduce a hierarchical magneto-responsive composite surface, made by infiltrating a ferrofluid into a microstructured matrix (termed ferrofluid-containing liquid-infused porous surfaces, or FLIPS). We demonstrate various topographical reconfigurations at multiple length scales and a broad range of associated emergent behaviours. An applied magnetic-field gradient induces the movement of magnetic nanoparticles suspended in the ferrofluid, which leads to microscale flow of the ferrofluid first above and then within the microstructured surface. This redistribution changes the initially smooth surface of the ferrofluid (which is immobilized by the porous matrix through capillary forces) into various multiscale hierarchical topographies shaped by the size, arrangement and orientation of the confining microstructures in the magnetic field. We analyse the spatial and temporal dynamics of these reconfigurations theoretically and experimentally as a function of the balance between capillary and magnetic pressures15-19 and of the geometric anisotropy of the FLIPS system. Several interesting functions at three different length scales are demonstrated: self-assembly of colloidal particles at the micrometre scale; regulated flow of liquid droplets at the millimetre scale; and switchable adhesion and friction, liquid pumping and removal of biofilms at the centimetre scale. We envision that FLIPS could be used as part of integrated control systems for the manipulation and transport of matter, thermal management, microfluidics and fouling-release materials.

pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Thumb xl selfsensing
Self-Sensing Paper Actuators Based on Graphite–Carbon Nanotube Hybrid Films

Amjadi, M., Sitti, M.

Advanced Science, pages: 1800239, May 2018 (article)

Abstract
Abstract Soft actuators have demonstrated potential in a range of applications, including soft robotics, artificial muscles, and biomimetic devices. However, the majority of current soft actuators suffer from the lack of real-time sensory feedback, prohibiting their effective sensing and multitask function. Here, a promising strategy is reported to design bilayer electrothermal actuators capable of simultaneous actuation and sensation (i.e., self-sensing actuators), merely through two input electric terminals. Decoupled electrothermal stimulation and strain sensation is achieved by the optimal combination of graphite microparticles and carbon nanotubes (CNTs) in the form of hybrid films. By finely tuning the charge transport properties of hybrid films, the signal-to-noise ratio (SNR) of self-sensing actuators is remarkably enhanced to over 66. As a result, self-sensing actuators can actively track their displacement and distinguish the touch of soft and hard objects.

pi

link (url) DOI Project Page [BibTex]


no image
Poster Abstract: Toward Fast Closed-loop Control over Multi-hop Low-power Wireless Networks

Mager, F., Baumann, D., Trimpe, S., Zimmerling, M.

Proceedings of the 17th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN), pages: 158-159, Porto, Portugal, April 2018 (poster)

ics

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Thumb xl f1.large
Soft erythrocyte-based bacterial microswimmers for cargo delivery

Alapan, Y., Yasa, O., Schauer, O., Giltinan, J., Tabak, A. F., Sourjik, V., Sitti, M.

Science Robotics, 3(17):eaar4423, Science Robotics, April 2018 (article)

Abstract
Bacteria-propelled biohybrid microswimmers have recently shown to be able to actively transport and deliver cargos encapsulated into their synthetic constructs to specific regions locally. However, usage of synthetic materials as cargo carriers can result in inferior performance in load-carrying efficiency, biocompatibility, and biodegradability, impeding clinical translation of biohybrid microswimmers. Here, we report construction and external guidance of bacteria-driven microswimmers using red blood cells (RBCs; erythrocytes) as autologous cargo carriers for active and guided drug delivery. Multifunctional biohybrid microswimmers were fabricated by attachment of RBCs [loaded with anticancer doxorubicin drug molecules and superparamagnetic iron oxide nanoparticles (SPIONs)] to bioengineered motile bacteria, Escherichia coli MG1655, via biotin-avidin-biotin binding complex. Autonomous and on-board propulsion of biohybrid microswimmers was provided by bacteria, and their external magnetic guidance was enabled by SPIONs loaded into the RBCs. Furthermore, bacteria-driven RBC microswimmers displayed preserved deformability and attachment stability even after squeezing in microchannels smaller than their sizes, as in the case of bare RBCs. In addition, an on-demand light-activated hyperthermia termination switch was engineered for RBC microswimmers to control bacteria population after operations. RBCs, as biological and autologous cargo carriers in the biohybrid microswimmers, offer notable advantages in stability, deformability, biocompatibility, and biodegradability over synthetic cargo-carrier materials. The biohybrid microswimmer design presented here transforms RBCs from passive cargo carriers into active and guidable cargo carriers toward targeted drug and other cargo delivery applications in medicine.

pi

link (url) DOI Project Page Project Page [BibTex]

link (url) DOI Project Page Project Page [BibTex]


Thumb xl testbed2
Evaluating Low-Power Wireless Cyber-Physical Systems

Baumann, D., Mager, F., Singh, H., Zimmerling, M., Trimpe, S.

In Proceedings of the IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), pages: 13-18, IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems (CPSBench), April 2018 (inproceedings)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


no image
Miniature soft robots – road to the clinic

Sitti, M.

Nature Reviews Materials, April 2018 (article)

Abstract
Soft small robots offer the opportunity to non-invasively access human tissue to perform medical operations and deliver drugs; however, challenges in materials design, biocompatibility and function control remain to be overcome for soft robots to reach the clinic.

pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]