Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. In the domain of reinforcement learning, control strategies are improved according to a reward function. The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated. Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. These strategies require feedback adapted to measurement outcomes. Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space. To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.
Organizers: Matthias Bauer
Theories of motor control in neuroscience usually focus on the role of the nervous system in the coordination of movement. However, the literature in sports science as well as in embodied robotics suggests that improvements in motor performance can be achieved through an improvement of the body mechanical properties themselves, rather than only the control. I therefore developed the thesis that efficient motor coordination in animals and humans relies on the adjustment of the body mechanical properties to the task at hand, by the postural system.
Über 1.000 selbstfahrende Testfahrzeuge von insgesamt 57 Unternehmen fahren im Silicon Valley bereits herum, und nun steht die Google-Schwester Waymo davor, 82.000 Robotertaxis auf die Straßen zu bringen. Und das nicht irgendwann, sondern noch dieses Jahr. Währenddessen rüstet sich Tesla mit seinem vollelektrischen Model 3 für einen Frontalangriff auf die deutschen Hersteller. In den USA sind die Verkaufszahlen deutscher Mittelklassewagen im Vergleich zum Vorjahr um 29 Prozent eingebrochen.
In this talk, I will take an autobiographical approach to explain both where we have come from in computer graphics from the early days of rendering, and to point towards where we are going in this new world of smartphones and social media. We are at a point in history where the abilities to express oneself with media is unparalleled. The ubiquity and power of mobile devices coupled with new algorithmic paradigms is opening new expressive possibilities weekly. At the same time, these new creative media (composite imagery, augmented imagery, short form video, 3D photos) also offer unprecedented abilities to move freely between what is real and unreal. I will focus on the spaces in between images and video, and in between objective and subjective reality. Finally, I will close with some lessons learned along the way.
The increasing availability of on-line resources and the widespread practice of storing data over the internet arise the problem of their accessibility for visually impaired people. A translation from the visual domain to the available modalities is therefore necessary to study if this access is somewhat possible. However, the translation of information from vision to touch is necessarily impaired due to the superiority of vision during the acquisition process. Yet, compromises exist as visual information can be simplified, sketched. A picture can become a map. An object can become a geometrical shape. Under some circumstances, and with a reasonable loss of generality, touch can substitute vision. In particular, when touch substitutes vision, data can be differentiated by adding a further dimension to the tactile feedback, i.e. extending tactile feedback to three dimensions instead of two. This mode has been chosen because it mimics our natural way of following object profiles with fingers. Specifically, regardless if a hand lying on an object is moving or not, our tactile and proprioceptive systems are both stimulated and tell us something about which object we are manipulating, what can be its shape and size. The goal of this talk is to describe how to exploit tactile stimulation to render digital information non visually, so that cognitive maps associated with this information can be efficiently elicited from visually impaired persons. In particular, the focus is to deliver geometrical information in a learning scenario. Moreover, a completely blind interaction with virtual environment in a learning scenario is something little investigated because visually impaired subjects are often passive agents of exercises with fixed environment constraints. For this reason, during the talk I will provide my personal answer to the question: can visually impaired people manipulate dynamic virtual content through touch? This process is much more challenging than only exploring and learning a virtual content, but at the same time it leads to a more conscious and dynamic creation of the spatial understanding of an environment during tactile exploration.
Organizers: Katherine J. Kuchenbecker
Continuum structures need to be designed for optimal vibrational characteristics in various fields. Recent developments in the finite element analysis (FEA) and numerical optimization methods allow creating more accurate computational models, which favors designing superior systems and reduces the need for experimentation. In this talk, I will present my work on FEA-based optimization of thin shell structures for improved dynamic properties where the focus will be on laminated composites. I will initially explain multi-objective optimization strategies for enhancing load-carrying and vibrational performance of plate structures. The talk will continue with the design of curved panels for optimal free and forced dynamic responses. After that, I will present advanced methods that I developed for modeling and optimization of variable-stiffness structures. Finally, I will outline the state-of-the-art techniques regarding numerical simulation of the finger in contact with surfaces and propose potential research directions.
Organizers: Katherine J. Kuchenbecker
This lecture will show some interesting examples how soft body/skin will change your idea of robotic sensing. Soft Robotics does not only discuss about compliance and safety; soft structure will change the way to categorize objects by dynamic exploration and enables the robot to learn sense of slip. Soft Robotics will entirely change your idea how to design sensing and open up a new way to understand human sensing.
Organizers: Ardian Jusufi
The FLEXMIN haptic robotic system is a single-port tele-manipulator for robotic surgery in the small pelvis. Using a transanal approach it allows bi-manual tasks such as grasping, monopolar cutting, and suturing with a footprint of Ø 160 x 240 mm³. Forces up to 5 N in all direction can be applied easily. In addition to provide low latency and highly dynamic control over its movements, high-fidelity haptic feedback was realised using built-in force sensors, lightweight and friction-optimized kinematics as well as dedicated parallel kinematics input devices. After a brief description of the system and some of its key aspects, first evaluation results will be presented. In the second half of the talk the Institute of Medical Device Technology will be presented. The institute was founded in July 2017 and has ever since started a number of projects in the field of biomedical actuation, medical systems and robotics and advanced light microscopy. To illustrate this a few snapshots of bits and pieces will be presented that are condensation nuclei for the future.
Organizers: Katherine J. Kuchenbecker
With the expanding collection of data, organisations are becoming more and more aware of the potential gain of combining their data. Analytic and predictive tasks, such as classification, perform more accurately if more features or more data records are available, which is why data providers have an interest in joining their datasets and learning from the obtained database. However, this rising interest for federated learning also comes with an increasing concern about security and privacy, both from the consumers whose data is used, and from the data providers who are liable for protecting it. Securely learning a classifier over joint datasets is a first milestone for private multi-party machine learning, and though some literature exists on that topic, systems providing a better security-utility trade-off and more theoretical guarantees are still needed. An ongoing issue is how to deal with the loss gradients, which often need to be revealed in the clear during training. We show that this constitutes an information leak, and present an alternative optimisation strategy that provides additional security guarantees while limiting the decrease in performance of the obtained classifier. Combining an encryption-based and a noise-based approach, the proposed method enables several parties to jointly train a binary classifier over vertically partitioned datasets while keeping their data private.
Organizers: Sebastian Trimpe
This talk presents an overview of recent activities of FEMTO-ST institute in the field of micro-nanomanipulation fo both micro nano assembly and biomedical applications. Microrobotic systems are currently limited by the number of degree of freedom addressed and also are very limited by their throughput. Two ways can be considered to improve both the velocity and the degrees of freedom: non-contact manipulation and dexterous micromanipulation. Indeed in both ways movement including rotation and translation are done locally and are only limited by the micro-nano-objects inertia which is very low. It consequently enable to generate 6DOF and to induce high dynamics. The talk presents recent works which have shown that controlled trajectories in non contact manipulation enable to manipulate micro-objects in high speed. Dexterous manipulation on a 4 fingers microtweezers have been also experimented and show that in-hand micromanipulations are possible in micro-nanoscale based on original finger trajectory planning. These two approaches have been applied to perform micro-nano-assemby and biomedical operations
In this talk I will be presenting recent work on combining ideas from deformable models with deep learning. I will start by describing DenseReg and DensePose, two recently introduced systems for establishing dense correspondences between 2D images and 3D surface models ``in the wild'', namely in the presence of background, occlusions, and multiple objects. For DensePose in particular we introduce DensePose-COCO, a large-scale dataset for dense pose estimation, and DensePose-RCNN, a system which operates at multiple frames per second on a single GPU while handling multiple humans simultaneously. I will then present Deforming AutoEncoders, a method for unsupervised dense correspondence estimation. We show that we can disentangle deformations from appearance variation in an entirely unsupervised manner, and also provide promising results for a more thorough disentanglement of images into deformations, albedo and shading. Time permitting we will discuss a parallel line of work aiming at combining grouping with deep learning, and see how both grouping and correspondence can be understood as establishing associations between neurons.
Organizers: Vassilis Choutas