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2020


Event-triggered Learning
Event-triggered Learning

Solowjow, F., Trimpe, S.

Automatica, 117, Elsevier, July 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

2020


arXiv PDF DOI Project Page [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.

ics

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


Statistical reprogramming of macroscopic self-assembly with dynamic boundaries
Statistical reprogramming of macroscopic self-assembly with dynamic boundaries

Culha, U., Davidson, Z. S., Mastrangeli, M., Sitti, M.

Proceedings of the National Academy of Sciences, 117(21):11306-11313, May 2020 (article)

Abstract
Self-assembly is a ubiquitous process that can generate complex and functional structures via local interactions among a large set of simpler components. The ability to program the self-assembly pathway of component sets elucidates fundamental physics and enables alternative competitive fabrication technologies. Reprogrammability offers further opportunities for tuning structural and material properties but requires reversible selection from multistable self-assembling patterns, which remains a challenge. Here, we show statistical reprogramming of two-dimensional (2D), noncompact self-assembled structures by the dynamic confinement of orbitally shaken and magnetically repulsive millimeter-scale particles. Under a constant shaking regime, we control the rate of radius change of an assembly arena via moving hard boundaries and select among a finite set of self-assembled patterns repeatably and reversibly. By temporarily trapping particles in topologically identified stable states, we also demonstrate 2D reprogrammable stiffness and three-dimensional (3D) magnetic clutching of the self-assembled structures. Our reprogrammable system has prospective implications for the design of granular materials in a multitude of physical scales where out-of-equilibrium self-assembly can be realized with different numbers or types of particles. Our dynamic boundary regulation may also enable robust bottom-up control strategies for novel robotic assembly applications by designing more complex spatiotemporal interactions using mobile robots.

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DOI [BibTex]

DOI [BibTex]


Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
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, 28(3):730-740, May 2020 (article)

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]


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Sliding Mode Control with Gaussian Process Regression for Underwater Robots

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

Journal of Intelligent & Robotic Systems, January 2020 (article)

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DOI [BibTex]

DOI [BibTex]


Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
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)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
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)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Thermal Effects on the Crystallization Kinetics, and Interfacial Adhesion of Single-Crystal Phase-Change Gallium
Thermal Effects on the Crystallization Kinetics, and Interfacial Adhesion of Single-Crystal Phase-Change Gallium

Yunusa, M., Lahlou, A., Sitti, M.

Advanced Materials, Wiley Online Library, 2020 (article)

Abstract
Although substrates play an important role upon crystallization of supercooled liquids, the influences of surface temperature and thermal property have remained elusive. Here, the crystallization of supercooled phase‐change gallium (Ga) on substrates with different thermal conductivity is studied. The effect of interfacial temperature on the crystallization kinetics, which dictates thermo‐mechanical stresses between the substrate and the crystallized Ga, is investigated. At an elevated surface temperature, close to the melting point of Ga, an extended single‐crystal growth of Ga on dielectric substrates due to layering effect and annealing is realized without the application of external fields. Adhesive strength at the interfaces depends on the thermal conductivity and initial surface temperature of the substrates. This insight can be applicable to other liquid metals for industrial applications, and sheds more light on phase‐change memory crystallization.

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[BibTex]


Nanoerythrosome-functionalized biohybrid microswimmers
Nanoerythrosome-functionalized biohybrid microswimmers

Buss, N., Yasa, O., Alapan, Y., Akolpoglu, M. B., Sitti, M.

APL Bioengineering, 4, AIP Publishing LLC, 2020 (article)

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[BibTex]

[BibTex]


Injectable Nanoelectrodes Enable Wireless Deep Brain Stimulation of Native Tissue in Freely Moving Mice
Injectable Nanoelectrodes Enable Wireless Deep Brain Stimulation of Native Tissue in Freely Moving Mice

Kozielski, K. L., Jahanshahi, A., Gilbert, H. B., Yu, Y., Erin, O., Francisco, D., Alosaimi, F., Temel, Y., Sitti, M.

bioRxiv, Cold Spring Harbor Laboratory, 2020 (article)

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[BibTex]

[BibTex]


Magnetically Actuated Soft Capsule Endoscope for Fine-Needle Biopsy
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 …, 2020 (article)

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[BibTex]

[BibTex]


Mechanical coupling of puller and pusher active microswimmers influences motility
Mechanical coupling of puller and pusher active microswimmers influences motility

Singh, A. V., Kishore, V., Santamauro, G., Yasa, O., Bill, J., Sitti, M.

Langmuir, ACS Publications, 2020 (article)

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[BibTex]


Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

ics am

arXiv code (python) PDF [BibTex]


Microribbons composed of directionally self-assembled nanoflakes as highly stretchable ionic neural electrodes
Microribbons composed of directionally self-assembled nanoflakes as highly stretchable ionic neural electrodes

Zhang, M., Guo, R., Chen, K., Wang, Y., Niu, J., Guo, Y., Zhang, Y., Yin, Z., Xia, K., Zhou, B., Wang, H., He, W., Liu, J., Sitti, M., Zhang, Y.

Proceedings of the National Academy of Sciences, National Academy of Sciences, 2020 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms
Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms

Dong, X., Sitti, M.

The International Journal of Robotics Research, 2020 (article)

Abstract
Magnetically actuated mobile microrobots can access distant, enclosed, and small spaces, such as inside microfluidic channels and the human body, making them appealing for minimally invasive tasks. Despite their simplicity when scaling down, creating collective microrobots that can work closely and cooperatively, as well as reconfigure their formations for different tasks, would significantly enhance their capabilities such as manipulation of objects. However, a challenge of realizing such cooperative magnetic microrobots is to program and reconfigure their formations and collective motions with under-actuated control signals. This article presents a method of controlling 2D static and time-varying formations among collective self-repelling ferromagnetic microrobots (100 μm to 350 μm in diameter, up to 260 in number) by spatially and temporally programming an external magnetic potential energy distribution at the air–water interface or on solid surfaces. A general design method is introduced to program external magnetic potential energy using ferromagnets. A predictive model of the collective system is also presented to predict the formation and guide the design procedure. With the proposed method, versatile complex static formations are experimentally demonstrated and the programmability and scaling effects of formations are analyzed. We also demonstrate the collective mobility of these magnetic microrobots by controlling them to exhibit bio-inspired collective behaviors such as aggregation, directional motion with arbitrary swarm headings, and rotational swarming motion. Finally, the functions of the produced microrobotic swarm are demonstrated by controlling them to navigate through cluttered environments and complete reconfigurable cooperative manipulation tasks.

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DOI [BibTex]


Magnetic Resonance Imaging System--Driven Medical Robotics
Magnetic Resonance Imaging System–Driven Medical Robotics

Erin, O., Boyvat, M., Tiryaki, M. E., Phelan, M., Sitti, M.

Advanced Intelligent Systems, 2, Wiley Online Library, 2020 (article)

Abstract
Magnetic resonance imaging (MRI) system–driven medical robotics is an emerging field that aims to use clinical MRI systems not only for medical imaging but also for actuation, localization, and control of medical robots. Submillimeter scale resolution of MR images for soft tissues combined with the electromagnetic gradient coil–based magnetic actuation available inside MR scanners can enable theranostic applications of medical robots for precise image‐guided minimally invasive interventions. MRI‐driven robotics typically does not introduce new MRI instrumentation for actuation but instead focuses on converting already available instrumentation for robotic purposes. To use the advantages of this technology, various medical devices such as untethered mobile magnetic robots and tethered active catheters have been designed to be powered magnetically inside MRI systems. Herein, the state‐of‐the‐art progress, challenges, and future directions of MRI‐driven medical robotic systems are reviewed.

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[BibTex]

[BibTex]


Characterization and Thermal Management of a DC Motor-Driven Resonant Actuator for Miniature Mobile Robots with Oscillating Limbs
Characterization and Thermal Management of a DC Motor-Driven Resonant Actuator for Miniature Mobile Robots with Oscillating Limbs

Colmenares, D., Kania, R., Liu, M., Sitti, M.

arXiv preprint arXiv:2002.00798, 2020 (article)

Abstract
In this paper, we characterize the performance of and develop thermal management solutions for a DC motor-driven resonant actuator developed for flapping wing micro air vehicles. The actuator, a DC micro-gearmotor connected in parallel with a torsional spring, drives reciprocal wing motion. Compared to the gearmotor alone, this design increased torque and power density by 161.1% and 666.8%, respectively, while decreasing the drawn current by 25.8%. Characterization of the actuator, isolated from nonlinear aerodynamic loading, results in standard metrics directly comparable to other actuators. The micro-motor, selected for low weight considerations, operates at high power for limited duration due to thermal effects. To predict system performance, a lumped parameter thermal circuit model was developed. Critical model parameters for this micro-motor, two orders of magnitude smaller than those previously characterized, were identified experimentally. This included the effects of variable winding resistance, bushing friction, speed-dependent forced convection, and the addition of a heatsink. The model was then used to determine a safe operation envelope for the vehicle and to design a weight-optimal heatsink. This actuator design and thermal modeling approach could be applied more generally to improve the performance of any miniature mobile robot or device with motor-driven oscillating limbs or loads.

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[BibTex]


Pros and Cons: Magnetic versus Optical Microrobots
Pros and Cons: Magnetic versus Optical Microrobots

Sitti, M., Wiersma, D. S.

Advanced Materials, Wiley Online Library, 2020 (article)

Abstract
Mobile microrobotics has emerged as a new robotics field within the last decade to create untethered tiny robots that can access and operate in unprecedented, dangerous, or hard‐to‐reach small spaces noninvasively toward disruptive medical, biotechnology, desktop manufacturing, environmental remediation, and other potential applications. Magnetic and optical actuation methods are the most widely used actuation methods in mobile microrobotics currently, in addition to acoustic and biological (cell‐driven) actuation approaches. The pros and cons of these actuation methods are reported here, depending on the given context. They can both enable long‐range, fast, and precise actuation of single or a large number of microrobots in diverse environments. Magnetic actuation has unique potential for medical applications of microrobots inside nontransparent tissues at high penetration depths, while optical actuation is suitable for more biotechnology, lab‐/organ‐on‐a‐chip, and desktop manufacturing types of applications with much less surface penetration depth requirements or with transparent environments. Combining both methods in new robot designs can have a strong potential of combining the pros of both methods. There is still much progress needed in both actuation methods to realize the potential disruptive applications of mobile microrobots in real‐world conditions.

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[BibTex]

[BibTex]


Selectively Controlled Magnetic Microrobots with Opposing Helices
Selectively Controlled Magnetic Microrobots with Opposing Helices

Giltinan, J., Katsamba, P., Wang, W., Lauga, E., Sitti, M.

Applied Physics Letters, 116, AIP Publishing LLC, 2020 (article)

pi

[BibTex]

[BibTex]


Microscale Polarization Color Pixels from Liquid Crystal Elastomers
Microscale Polarization Color Pixels from Liquid Crystal Elastomers

Guo, Y., Shahsavan, H., Sitti, M.

Advanced Optical Materials, Wiley Online Library, 2020 (article)

pi

[BibTex]

[BibTex]


Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications
Wearable and Stretchable Strain Sensors: Materials, Sensing Mechanisms, and Applications

Souri, H., Banerjee, H., Jusufi, A., Radacsi, N., Stokes, A. A., Park, I., Sitti, M., Amjadi, M.

Advanced Intelligent Systems, 2020 (article)

bio pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Ultrasound-guided Wireless Tubular Robotic Anchoring System
Ultrasound-guided Wireless Tubular Robotic Anchoring System

Wang, T., Hu, W., Ren, Z., Sitti, M.

IEEE Robotics and Automation Letters, 5, pages: 4859 - 4866, IEEE, 2020 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Cohesive self-organization of mobile microrobotic swarms
Cohesive self-organization of mobile microrobotic swarms

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

arXiv preprint arXiv:1907.05856, 2020 (article)

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[BibTex]

[BibTex]


Bio-inspired Flexible Twisting Wings Increase Lift and Efficiency of a Flapping Wing Micro Air Vehicle
Bio-inspired Flexible Twisting Wings Increase Lift and Efficiency of a Flapping Wing Micro Air Vehicle

Colmenares, D., Kania, R., Zhang, W., Sitti, M.

arXiv preprint arXiv:2001.11586, 2020 (article)

Abstract
We investigate the effect of wing twist flexibility on lift and efficiency of a flapping-wing micro air vehicle capable of liftoff. Wings used previously were chosen to be fully rigid due to modeling and fabrication constraints. However, biological wings are highly flexible and other micro air vehicles have successfully utilized flexible wing structures for specialized tasks. The goal of our study is to determine if dynamic twisting of flexible wings can increase overall aerodynamic lift and efficiency. A flexible twisting wing design was found to increase aerodynamic efficiency by 41.3%, translational lift production by 35.3%, and the effective lift coefficient by 63.7% compared to the rigid-wing design. These results exceed the predictions of quasi-steady blade element models, indicating the need for unsteady computational fluid dynamics simulations of twisted flapping wings.

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[BibTex]

[BibTex]


Acoustically powered surface-slipping mobile microrobots
Acoustically powered surface-slipping mobile microrobots

Aghakhani, A., Yasa, O., Wrede, P., Sitti, M.

Proceedings of the National Academy of Sciences, 117, National Acad Sciences, 2020 (article)

Abstract
Untethered synthetic microrobots have significant potential to revolutionize minimally invasive medical interventions in the future. However, their relatively slow speed and low controllability near surfaces typically are some of the barriers standing in the way of their medical applications. Here, we introduce acoustically powered microrobots with a fast, unidirectional surface-slipping locomotion on both flat and curved surfaces. The proposed three-dimensionally printed, bullet-shaped microrobot contains a spherical air bubble trapped inside its internal body cavity, where the bubble is resonated using acoustic waves. The net fluidic flow due to the bubble oscillation orients the microrobot's axisymmetric axis perpendicular to the wall and then propels it laterally at very high speeds (up to 90 body lengths per second with a body length of 25 µm) while inducing an attractive force toward the wall. To achieve unidirectional locomotion, a small fin is added to the microrobot’s cylindrical body surface, which biases the propulsion direction. For motion direction control, the microrobots are coated anisotropically with a soft magnetic nanofilm layer, allowing steering under a uniform magnetic field. Finally, surface locomotion capability of the microrobots is demonstrated inside a three-dimensional circular cross-sectional microchannel under acoustic actuation. Overall, the combination of acoustic powering and magnetic steering can be effectively utilized to actuate and navigate these microrobots in confined and hard-to-reach body location areas in a minimally invasive fashion.

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[BibTex]

[BibTex]


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Morphology-Dependent Immunogenicity Obliges a Compromise on the Locomotion-Focused Design of Medical Microrobots

Ceren, , Hakan, , Ugur, , Anna-Maria, , Metin,

Science Robotics, 2020 (article) Accepted

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[BibTex]

[BibTex]


Selection for Function: From Chemically Synthesized Prototypes to 3D-Printed Microdevices
Selection for Function: From Chemically Synthesized Prototypes to 3D-Printed Microdevices

Bachmann, F., Giltinan, J., Codutti, A., Klumpp, S., Sitti, M., Faivre, D.

Advanced Intelligent Systems, 2020 (article)

pi

[BibTex]

[BibTex]


Biosynthetic self-healing materials for soft machines
Biosynthetic self-healing materials for soft machines

Pena-Francesch, A., Jung, H., Demirel, M. C., Sitti, M.

Nature Materials , 2020 (article)

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DOI [BibTex]

DOI [BibTex]


Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage
Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

Haksar, R. N., Trimpe, S., Schwager, M.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

ics

DOI [BibTex]

DOI [BibTex]


Bioinspired underwater locomotion of light-driven liquid crystal gels
Bioinspired underwater locomotion of light-driven liquid crystal gels

Shahsavan, H., Aghakhani, A., Zeng, H., Guo, Y., Davidson, Z. S., Priimagi, A., Sitti, M.

Proceedings of the National Academy of Sciences, National Acad Sciences, 2020 (article)

Abstract
Untethered dynamic shape programming and control of soft materials have significant applications in technologies such as soft robots, medical devices, organ-on-a-chip, and optical devices. Here, we present a solution to remotely actuate and move soft materials underwater in a fast, efficient, and controlled manner using photoresponsive liquid crystal gels (LCGs). LCG constructs with engineered molecular alignment show a low and sharp phase-transition temperature and experience considerable density reduction by light exposure, thereby allowing rapid and reversible shape changes. We demonstrate different modes of underwater locomotion, such as crawling, walking, jumping, and swimming, by localized and time-varying illumination of LCGs. The diverse locomotion modes of smart LCGs can provide a new toolbox for designing efficient light-fueled soft robots in fluid-immersed media.

pi

[BibTex]

[BibTex]


Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

am ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients
Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients

Giachini, P., Gupta, S., Wang, W., Wood, D., Yunusa, M., Baharlou, E., Sitti, M., Menges, A.

Science Advances, 6, American Association for the Advancement of Science, 2020 (article)

Abstract
Functionally graded materials (FGMs) enable applications in fields such as biomedicine and architecture, but their fabrication suffers from shortcomings in gradient continuity, interfacial bonding, and directional freedom. In addition, most commercial design software fail to incorporate property gradient data, hindering explorations of the design space of FGMs. Here, we leveraged a combined approach of materials engineering and digital processing to enable extrusion-based multimaterial additive manufacturing of cellulose-based tunable viscoelastic materials with continuous, high-contrast, and multidirectional stiffness gradients. A method to engineer sets of cellulose-based materials with similar compositions, yet distinct mechanical and rheological properties, was established. In parallel, a digital workflow was developed to embed gradient information into design models with integrated fabrication path planning. The payoff of integrating these physical and digital tools is the ability to achieve the same stiffness gradient in multiple ways, opening design possibilities previously limited by the rigid coupling of material and geometry.

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[BibTex]

[BibTex]


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Introducing Progress in Biomedical Engineering; Issue 2 Vol 2

Sitti, M.

Progress in Biomedical Engineering, IOP Publishing, 2020 (article)

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[BibTex]

[BibTex]


Multi-wavelength steerable visible light-driven magnetic CoO-TiO2 microswimmers
Multi-wavelength steerable visible light-driven magnetic CoO-TiO2 microswimmers

Sridhar, V., Park, B., Guo, S., van Aken, P. A., Sitti, M.

ACS Applied Materials \& Interfaces, ACS Publications, 2020 (article)

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[BibTex]

[BibTex]

2018


Swimming Back and Forth Using Planar Flagellar Propulsion at Low Reynolds Numbers
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.

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link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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.

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DOI [BibTex]

DOI [BibTex]


3D-Printed Biodegradable Microswimmer for Drug Delivery and Targeted Cell Labeling
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]


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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]


Innate turning preference of leaf-cutting ants in the absence of external orientation cues
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]


Motility and chemotaxis of bacteria-driven microswimmers fabricated using antigen 43-mediated biotin display
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]


Multifunctional ferrofluid-infused surfaces with reconfigurable multiscale topography
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]