Biomimetic collective behaviour with active matter
Morphology and gait learning with soft milirobots
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.
PhD: Mechanical Engineering, ETH Zürich, Switzerland, 2016
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems