Standard methods of causal discovery take as input a statistical data set of measurements of well-defined causal variables. The goal is then to determine the causal relations among these variables. But how are these causal variables identified or constructed in the first place? Often we have sensor level data but assume that the relevant causal interactions occur at a higher scale of aggregation. Sometimes we only have aggregate measurements of causal interactions at a finer scale. I will motivate the general problem of causal discovery and present recent work on a framework and method for the construction and identification of causal macro-variables that ensures that the resulting causal variables have well-defined intervention distributions. Time permitting, I will show an application of this approach to large scale climate data, for which we were able to identify the macro-phenomenon of El Nino using an unsupervised method on micro-level measurements of the sea surface temperature and wind speeds over the equatorial Pacific.
Organizers: Sebastian Weichwald
I am studying the question how robots can autonomously develop skills. Considering children, it seems natural that they have their own agenda. They explore their environment in a playful way, without the necessity for somebody to tell them what to do next. With robots the situation is different. There are many methods to let robots learn to do something, but it is always about learning to do a specific task from a supervision signal. Unfortunately, these methods do not scale well to systems with many degrees of freedom, except a good prestructuring is available. The hypothesis is that if the robots first learn to use their bodies and interact with the environment in a playful way they can acquire many small skills with which they can later solve complicated tasks much quicker. In the talk I will present my steps into this direction. Starting from some general information theoretic consideration we provide robots with an own drive to do something and explore their behavioral capabilities. Technically this is achieved by considering the sensorimotor loop as a dynamical system, whose parameters are adapted online according to a gradient ascent on an approximated information quantity. I will show examples of simulated and real robots behaving in a self-determined way and present future directions of my research.
Organizers: Jane Walters
In the last decade, there has been a major shift in the perception, use and predicted applications of robots. In contrast to their early industrial counterparts, robots are envisioned to operate in increasingly complex and uncertain environments, alongside humans, and over long periods of time. In my talk, I will argue that machine learning is indispensable in order for this new generation of robots to achieve high performance. Based on various examples (and videos) ranging from aerial-vehicle dancing to ground-vehicle racing, I will demonstrate the effect of robot learning, and highlight how our learning algorithms intertwine model-based control with machine learning. In particular, I will focus on our latest work that provides guarantees during learning (for example, safety and robustness guarantees) by combining traditional controls methods (nonlinear, robust and model predictive control) with Gaussian process regression.
Organizers: Sebastian Trimpe
In this talk we present some recent results on human action recognition in videos. We, first, show how to use human pose for action recognition. To this end we propose a new pose-based convolutional neural network descriptor for action recognition, which aggregates motion and appearance information along tracks of human body parts. Next, we present an approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and then tracks high-scoring proposals in the video. Our tracker relies simultaneously on instance-level and class-level detectors. Action are localized in time with a sliding window approach at the track level. Finally, we show how to extend this method to weakly supervised learning of actions, which allows to scale to large amounts of data without manual annotation.
Typical human actions such as hand-shaking and drinking last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of single frames or short video clips and fail to model actions at their full temporal scale. In this work we learn video representations using neural networks with long-term temporal convolutions. We demonstrate that CNN models with increased temporal extents improve the accuracy of action recognition despite reduced spatial resolution. We also study the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields and demonstrate the importance of high-quality optical flow estimation for learning accurate action models. We report state-of-the-art results on two challenging benchmarks for human action recognition UCF101 and HMDB51.
Proper handling of occlusions is a big challenge for model based reconstruction, e.g. for multi-view motion capture a major difficulty is the handling of occluding body parts. We propose a smooth volumetric scene representation, which implicitly converts occlusion into a smooth and differentiable phenomena (ICCV2015). Our ray tracing image formation model helps to express the objective in a single closed-form expression. This is in contrast to existing surface(mesh) representations, where occlusion is a local effect, causes non-differentiability, and is difficult to optimize. We demonstrate improvements for multi-view scene reconstruction, rigid object tracking, and motion capture. Moreover, I will show an application of motion tracking to the interactive control of virtual characters (SigAsia2015).
The core focus of my research is on robot perception. Within this broad categorization, I am mainly interested in understanding how teams of robots and sensors can cooperate and/or collaborate to improve the perception of themselves (self-localization) as well as their surroundings (target tracking, mapping, etc.). In this talk I will describe the inter-dependencies of such perception modules and present state-of-the-art methods to perform unified cooperative state estimation. The trade-off between accuracy of estimation and computational speed will be highlighted through a new optimization-based method for unified-state estimation. Furthermore, I will also describe how perception-based multirobot formation control can be achieved. Towards the end, I will present some recent results on cooperative vision-based target tracking and a few comments on our ongoing work regarding cooperative aerial mapping with human-in-the-loop.
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.