There has been significant prior work on learning realistic, articulated, 3D statistical shape models of the human body. In contrast, there are few such models for animals, despite their many applications in biology, neuroscience, agriculture, and entertainment. The main challenge is that animals are much less cooperative subjects than humans: the best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. In the talk I will illustrate how we extend a state-of-the-art articulated 3D human body model (SMPL) to animals learning from toys a multi-family shape space that can represent lions, cats, dogs, horses, cows and hippos. The generalization of the model is illustrated by fitting it to images of real animals, where it captures realistic animal shapes, even for new species not seen in training.
Modeling and reconstruction of shape and motion are problems of fundamental importance in computer vision. Inverse Problem theory constitutes a powerful mathematical framework for dealing with ill-posed problems as the ones typically arising in shape and motion modeling. In this talk, I will present methods inspired by Inverse Problem theory, for dealing with four different shape and motion modeling problems. In particular, in the context of shape modeling, I will present a method for component-wise modeling of articulated objects and its application in computing 3D models of animals. Additionally, I will discuss the problem of modeling of specular surfaces via the properties of their material, and I will also present a model for confidence driven depth image fusion based on total variation regularization. Regarding motion, I will discuss a method for the recognition of human actions from motion capture data based on Nonparametric Bayesian models.
Computer vision on flying robots - or UAVs - brings its own challenges, especially if conducted in real time. On-board processing is limited by tight weight and size constraints for the electronics while off-board processing is challenged by signal delays and connection quality, especially considering the data rates required for high fps high resolution video. Unlike ground based vehicles, precision odometry is unavailable. Positional information is provided by GPS, which can have signal losses and limited precision, especially near terrain. Exact orientation can be even more problematic due to magnetic interference and vibration affecting sensors. In this talk I'd like to present and discuss some examples of practical problems encountered when trying to get robotics airborne – as well as possible solutions.
Organizers: Alina Allakhverdieva
The development of increasingly intelligent and autonomous technologies will inevitably lead to these systems having to face morally problematic situations. This is particularly true of artificial systems that are used in geriatric care environments. It will, therefore, be necessary in the long run to develop machines which have the capacity for a certain amount of autonomous moral decision-making. The goal of this talk is to provide the theoretical foundations for artificial morality, i.e., for implementing moral capacities in artificial systems in general and a roadmap for developing an assistive system in geriatric care which is capable of moral learning.
Inverse problems are ubiquitous in image processing and applied science in general. Such problems describe the challenge of computing the parameters that characterize a system from the outcomes. While this might seem easy at first for simple systems, many inverse problems share a property that makes them much more intricate: they are ill-posed. This means that either the problem does not have a unique solution or this solution does not depend continuously on the outcomes of the system. Bayesian statistics provides a framework that allows to treat such problems in a systematic way. The missing piece of information is encoded as a prior distribution on the space of possible solutions. In this talk, we will study probabilistic image models as priors for statistical inversion. In particular, we will give a probabilistic interpretation of the classical TV-prior and discuss how this interpretation can be used as a starting point for more complex models. We will see that many important auxiliary quantities such as edges and regions can be incorporated into the model in the form of latent variables. This leads to the conjecture that many image processing tasks, such as denoising and segmentation, should not be considered separately, but instead be treated together.
The detection and characterization of planets orbiting other stars than the Sun, i.e., so-called extrasolar planets, is one of the fastest growing and most vibrant research fields in modern astrophysics. In the last 25 years, more than 5400 extrasolar planets and planet candidates were revealed, but the vast majority of these objects was detected with indirect techniques, where the existence of the planet is inferred from periodic changes in the light coming from the central star. No photons from the planets themselves are detected. In this talk, however, I will focus on the direct detection of extrasolar planets. On the one hand I will describe the main challenges that have to be overcome in order to image planets around other stars. In addition to using the world’s largest telescopes and optimized cameras it was realized in last few years that by applying advanced image processing techniques significant sensitivity gains can be achieved. On the other hand I will demonstrate what can be learned if one is successful in “taking a picture” of an extrasolar planet. After all, there must be good scientific reasons and a strong motivation why the direct detection of extrasolar planets is one of the key science drivers for current and future projects on major ground- and space-based telescopes.
Organizers: Diana Rebmann
In general Helga Griffiths is a Multi-Sense-Artist working on the intersection of science and art. She has been working for over 20 years on the integration of various sensory stimuli into her “multi-sense” installations. Typical for her work is to produce a sensory experience to transcend conventional boundaries of perception.
Organizers: Emma-Jayne Holderness
Bayesian optimization is a powerful tool that has been successfully used to automatically optimize the parameters of a fixed control policy. It has many desirable properties, such as data-efficiently and being able to handle noisy measurements. However, standard Bayesian optimization does not consider any constraints imposed by the real system, which limits its applications to highly controlled environments. In this talk, I will introduce an extension of this framework, which additionally considers multiple safety constraints during the optimization process. This method enables safe parameter optimization by only evaluating parameters that fulfill all safety constraints with high probability. I will show several experiments on a quadrotor vehicle which demonstrate the method. Lastly, I will briefly talk about how the ideas behind safe Bayesian optimization can be used to safely explore unknown environments (MDPs).
Organizers: Sebastian Trimpe
Our research questions are centred on a basic characteristic of human brains: variability in their behaviour and their underlying meaning for cognitive mechanisms. Such variability is emerging as a key ingredient in understanding biological principles (Faisal, Selen & Wolpert, 2008, Nature Rev Neurosci) and yet lacks adequate quantitative and computational methods for description and analysis. Crucially, we find that biological and behavioural variability contains important information that our brain and our technology can make us of (instead of just averaging it away): Using advanced body sensor networks, we measured eye-movements, full-body and hand kinematics of humans living in a studio flat and are going to present some insightful results on motor control and visual attention that suggest that the control of behaviour "in-the-wild" is predictably different ways than what we measure "in-the-lab". The results have implications for robotics, prosthetics and neuroscience.
Organizers: Matthias Hohmann
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
Understanding the principles of natural movement generation has been and continues to be one of the most interesting and important open problems in the fields of robotics and neural control of movement. In this talk, I introduce an overview of our previous work on the control of dynamic movements in robotic systems towards the goal of control design principles and understanding of motion generation. Our research has focused in the fields of dynamical systems theory, adaptive and optimal control and statistical learning, and their application to robotics towards achieving dynamically dexterous behavior in robotic systems. First, our studies on dynamical systems based task encoding in robot brachiation, movement primitives for imitation learning, and oscillator based biped locomotion control will be presented. Then, our recent work on optimal control of robotic systems with variable stiffness actuation will be introduced towards the aim of achieving highly dynamic movements by exploiting the natural dynamics of the system. Finally, our new humanoid robot H-1 at TUM-ICS will be introduced.
Organizers: Ludovic Righetti