Programmable self-assembly of miniature soft robots
Using machine-learning to explore soft robot designs and functions
Bio-inspired collective behaviour with active matter
I am interested in creating soft robotic machines that can physically adapt to their environment by deforming their body morphologies. In biology, structural (physical) adaptation is explained by linking the emergence of functions (e.g. cell motility, cell division and morphogenesis) to the formation of asymmetric structures occurring in (sub) cellular scales. The collective behaviour of these neighbouring cells can then lead to the fundamentals of adaptation such as sensing, actuation and growth. I have previously worked on mechanisms which generate asymmetric deformations on soft thermoplastic materials for adaptive robotic sensing, locomotion and manipulation. Here, I would like to adopt a bottom-up approach similar to biology and achieve physical adaptation in much smaller scales. I believe that life-like and co-operative intelligent robotic systems can systematically be created once the mechanisms of adaptive function generation are established in small scale collective systems.
(2018-2020) Humboldt Research Fellowship: Postdoctoral research fellowship for conducting research in Germany for 24 months, Alexander von Humboldt Foundation, Germany.
(2018-2019) Grassroots Initiative Grant: Grant funding for the proposed project: Applying machine learning methods on self-assembling miniature soft robots (in collaboration with Dr. Sebastian Trimpe, Intelligent Control Systems), Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
DrSc.: Mechanical and Process Engineering, ETH Zürich, Switzerland, 2016
In Proceedings of Robotics: Science and Systems, July 2020, Culha and Demir are equally contributing authors (inproceedings)
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.
Proceedings of the National Academy of Sciences, 117(21):11306-11313, May 2020 (article)
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.
Unser Ziel ist es, die Prinzipien von Wahrnehmen, Lernen und Handeln in autonomen Systemen zu verstehen, die mit komplexen Umgebungen interagieren. Das Verständnis wollen wir nutzen, um künstliche intelligente Systeme zu entwickeln.