Machine learning allows automated systems to identify structures and physical laws based on measured data, which is particularly useful in areas where an analytic derivation of a model is too tedious or not possible. Research in reinforcement learning led to impressive results and superhuman performance in well-structured tasks and games. However, to this day, data-driven models are rarely employed in the control of safety critical systems, because the success of a controller, which is based on these models, cannot be guaranteed. Therefore, the research presented in this talk analyzes the closed-loop behavior of learning control laws by means of rigorous proofs. More specifically, we propose a control law based on Gaussian process (GP) models, which actively avoids uncertainties in the state space and favors trajectories along the training data, where the system is well-known. We show that this behavior is optimal as it maximizes the probability of asymptotic stability. Additionally, we consider an event-triggered online learning control law, which safely explores an initially unknown system. It only takes new training data whenever the uncertainty in the system becomes too large. As the control law only requires a locally precise model, this novel learning strategy has a high data efficiency and provides safety guarantees.
Organizers: Sebastian Trimpe
In this talk I will present an overview of our recent works that learn deep geometric models for the 3D face from large datasets of scans. Priors for the 3D face are crucial for many applications: to constrain ill posed problems such as 3D reconstruction from monocular input, for efficient generation and animation of 3D virtual avatars, or even in medical domains such as recognition of craniofacial disorders. Generative models of the face have been widely used for this task, as well as deep learning approaches that have recently emerged as a robust alternative. Barring a few exceptions, most of these data-driven approaches were built from either a relatively limited number of samples (in the case of linear models of the shape), or by synthetic data augmentation (for deep-learning based approaches), mainly due to the difficulty in obtaining large-scale and accurate 3D scans of the face. Yet, there is a substantial amount of 3D information that can be gathered when considering publicly available datasets that have been captured over the last decade. I will discuss here our works that tackle the challenges of building rich geometric models out of these large and varied datasets, with the goal of modeling the facial shape, expression (i.e. motion) or geometric details. Concretely, I will talk about (1) an efficient and fully automatic approach for registration of large datasets of 3D faces in motion; (2) deep learning methods for modeling the facial geometry that can disentangle the shape and expression aspects of the face; and (3) a multi-modal learning approach for capturing geometric details from images in-the-wild, by simultaneously encoding both facial surface normal and natural image information.
Organizers: Jinlong Yang
Motivated by the low voltage driven actuation of ionic Electroactive Polymers (iEAPs)  , recently we began investigating ionic elastomers. In this talk I will discuss the preparation, physical characterization and electric bending actuation properties of two novel ionic elastomers; ionic polymer electrolyte membranes (iPEM), and ionic liquid crystal elastomers (iLCE). Both materials can be actuated by low frequency AC or DC voltages of less than 1 V. The bending actuation properties of the iPEMs are outperforming most of the well-developed iEAPs, and the not optimized first iLCEs are already comparable to them. Ionic liquid crystal elastomers also exhibit superior features, such as the alignment dependent actuation, which offers the possibility of pre-programed actuation pattern at the level of cross-linking process. Additionally, multiple (thermal, optical and electric) actuations are also possible. I will also discuss issues with compliant electrodes and possible soft robotic applications.  Y. Bar-Cohen, Electroactive Polyer Actuators as Artficial Muscles: Reality, Potential and Challenges, SPIE Press, Bellingham, 2004.  O. Kim, S. J. Kim, M. J. Park, Chem. Commun. 2018, 54, 4895.  C. P. H. Rajapaksha, C. Feng, C. Piedrahita, J. Cao, V. Kaphle, B. Lüssem, T. Kyu, A. Jákli, Macromol. Rapid Commun. 2020, in print.  C. Feng, C. P. H. Rajapaksha, J. M. Cedillo, C. Piedrahita, J. Cao, V. Kaphle, B. Lussem, T. Kyu, A. I. Jákli, Macromol. Rapid Commun. 2019, 1900299.
“There’s something about the outside of a horse that is good for the inside of a man”, Churchill allegedly said. The horse’s motion has captured the interest of humans throughout history. Understanding of the mechanics of horse motion has been sought in early work by Aristotle (300 BC), in pioneering photographic studies by Muybridge (1880) as well as in modern day scientific publications.
The horse (Equus callabus ferus) is a remarkable animal athlete with outstanding running capabilities. The efficiency of its locomotion is explained by specialised anatomical features, which limit the degrees of freedom of movement and reduce energy consumption. Theoretical mechanical models are quite well suited to describe the essence of equine gaits and provide us with simple measures for analysing gait asymmetry. Such measures are well needed, since agreement between veterinarians is moderate to poor when it comes to visual assessment of lameness.
The human visual system has indeed clear limitations in perception and interpretation of horse motion. This limits our abilities to understand the horse, not only to detect lameness and to predict performance, but also to interpret its non-verbal communication and to detect signs of illness or discomfort.
This talk will provide a brief overview of existing motion analysis techniques and models in equine biomechanics. We will discuss future possibilities to achieve more accessible, sensitive and complex ways of analysing the motion of the horse.
Beginning with a seminal paper of Diaconis (1988), the aim of so-called "probabilistic numerics" is to compute probabilistic solutions to deterministic problems arising in numerical analysis by casting them as statistical inference problems. For example, numerical integration of a deterministic function can be seen as the integration of an unknown/random function, with evaluations of the integrand at the integration nodes proving partial information about the integrand. Advantages offered by this viewpoint include: access to the Bayesian representation of prior and posterior uncertainties; better propagation of uncertainty through hierarchical systems than simple worst-case error bounds; and appropriate accounting for numerical truncation and round-off error in inverse problems, so that the replicability of deterministic simulations is not confused with their accuracy, thereby yielding an inappropriately concentrated Bayesian posterior. This talk will describe recent work on probabilistic numerical solvers for ordinary and partial differential equations, including their theoretical construction, convergence rates, and applications to forward and inverse problems. Joint work with Andrew Stuart (Warwick).
Organizers: Philipp Hennig