Our group has broad interests in the interaction of optical, electric, and magnetic fields with matter at small length scales. We work on new 3-D fabrication methods, self-assembly, actuation, and propulsion. We have observed a number of fundamental effects and are developing new experimental techniques and instruments. [more]
Intelligent autonomous systems must be able to perceive their environment efficiently and robustly in order to navigate in our complex and changing world. The major challenge for mobile perception is to transform the huge amounts of ambiguous and incomplete measurements into easily accessible and compact representations. Besides, for 3D interpretation of the scene, the information lost by the projection process of the camera must be recovered, thus requiring to solve an inverse problem. [more]
The research group studies and develops numerical methods for use in intelligent systems. At the core of our approach lies the observation that algorithms for the computation of intractable objects, like integrals and extrema, can be phrased as active inference, as learning machines. [more]
We are interested in the statistical physics of systems out of equilibrium, which we aim to describe and understand with both analytical and numerical techniques. We focus on the quantum-fluctuations of electromagnetic fields, e.g. in connection to Casimir interactions and radiative heat transfer. We also study classical systems such as colloidal suspensions driven far from equilibrium. [more]
The nanometer scale is where the chemistry, biology, and materials sciences converge. The subtopic of nanoplasmonics deals with localization and manipulation of light in a nanometer volume. The key material component for plasmonics is metals. The optical properties of metal nanoparticles have been an object of fascination since ancient times. [more]
Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. While this aim has been a long-standing vision of artificial intelligence and the cognitive sciences, we have yet to achieve the first step of creating robots that can learn to accomplish many different tasks triggered by environmental context or higher-level instruction. [more]
What are the algorithmic principles that would allow a robot to run through a rocky terrain, lift a couch while reaching for an object that rolled under it or manipulate a screwdriver while balancing on top of a ladder? By answering these questions, we try to understand the fundamental principles for robotic locomotion and manipulation that will endow robots with the robustness and adaptability necessary to efficiently and autonomously act in an unknown and changing environment.
Our group has particular interest in the design of miniaturized devices that bridge multidisciplinary fields from material science, chemistry and biology. We aim to study a broad range of phenomena occurring at the interface between materials and biology, from fundamental studies to applications. Examples of those devices can be either integrated (bio)sensors in microfluidic chips or self-propelled nanorobots. [more]
We work on the bioinspired and biomimicking aspects of locomotion in robots, bioinspired approaches to sensor design, locomotion learning, and biomechanical aspects of locomotion in animals and robotic machines. [more]
In many modern optical appliances unwanted light reflections reduce the image quality notably. Nocturnal moths have solved this problem million of years ago. A nanometre-sized structure on the surface of their eyes results in almost perfect anti-reflective properties. In the nanoAR workgroup we are developing new cost efficient methods to coat commercially available surfaces with a similar, biomimetic nanostructure.