Events & Talks

Autonomous Motion Talk Mirko Bordignon 04-08-2017 Challenges of writing and maintaining programs for robots Writing and maintaining programs for robots poses some interesting challenges. It is hard to generalize them, as their targets are more than computing platforms. It can be deceptive to see them as input to output mappings, as interesting environments result in unpredictable inputs, and mixing reactive and deliberative behavior make intended outputs hard to define. Given the wide and fragmented landscape of components, from hardware to software, and the parties involved in providing and using them, integration is also a non-trivial aspect. The talk will illustrate the work ongoing at Fraunh... Vincent Berenz

Empirical Inference Talk Manfred K. Warmuth 25-07-2017 Low-dimensional Data Embedding via Robust Ranking Bernhard Schölkopf

Empirical Inference Talk Ioannis Papantonis 24-07-2017 Adaptive Learning Rate Algorithms for Stochastic Optimization and Variational Bayesian Inference We present a way to set the step size of Stochastic Gradient Descent, as the solution of a distance minimization problem. The obtained result has an intuitive interpretation and resembles the update rules of well known optimization algorithms. Also, asymptotic results to its relation to the optimal learning rate of Gradient Descent are discussed. In addition, we talk about two different estimators, with applications in Variational inference problems, and present approximate results about their variance. Finally, we combine all of the above, to present an optimization algorithm that... Philipp Hennig

Autonomous Motion Workshop 16-07-2017 Articulated Model Tracking Workshop at the RSS (Robotics: Science and Systems Conference) at the Kresge Auditorium at the Massachusetts Institute of Technology in Cambridge, Massachusetts, USA. Jeannette Bohg

Autonomous Motion Workshop 15-07-2017 Revisiting Contact - Turning a Problem into a Solution Workshop July 17, 2017 during RSS (Robotics: Science and Systems Conference) at the Kresge Auditorium at the Massachusetts Institute of Technology in Cambridge, Massachusetts, USA. Jeannette Bohg

Autonomous Motion Workshop 15-07-2017 Women in Robotics III Workshop at the RSS (Robotics: Science and Systems Conference) at the Kresge Auditorium at the Massachusetts Institute of Technology in Cambridge, Massachusetts, USA. Jeannette Bohg

Autonomous Vision Talk Matteo Poggi 12-07-2017 Deep Learning for stereo matching and related tasks Recently, deep learning proved to be successful also on low level vision tasks such as stereo matching. Another recent trend in this latter field is represented by confidence measures, with increasing effectiveness when coupled with random forest classifiers or CNNs. Despite their excellent accuracy in outliers detection, few other applications rely on them. In the first part of the talk, we'll take a look at the latest proposal in terms of confidence measures for stereo matching, as well as at some novel methodologies exploiting these very accurate cues. In the second part, we'll talk ab... Yiyi Liao

Talk Prof. Andrew Blake 12-07-2017 Machines that learn to see and move Neural networks have taken the world of computing in general and AI in particular by storm. But in the future, AI will need to revisit generative models. There are several reasons for this – system robustness, precision, transparency, and the high cost of labelling data. This is particularly true of perceptual AI, as needed for autonomous vehicles, where also the need for simulators and the need to confront novel situations, also will demand generative, probabilistic models. Bernhard Schölkopf Michael Black Stefan Schaal

Empirical Inference Talk Prof. Stéphanie Lacour 11-07-2017 Soft bioelectronics: Materials and Technology Bioelectronics integrates principles of electrical engineering and materials science to biology, medicine and ultimately health. Soft bioelectronics focus on designing and manufacturing electronic devices with mechanical properties close to those of the host biological tissue so that long-term reliability and minimal perturbation are induced in vivo and/or truly wearable systems become possible. We illustrate the potential of this soft technology with examples ranging from prosthetic tactile skins to soft multimodal neural implants.

Empirical Inference Talk Chris Bauch 10-07-2017 Sentiment analysis of tweets to detect tipping points in vaccinating behaviour Vaccine refusal can lead to outbreaks of previously eradicated diseases and is an increasing problem worldwide. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Complex systems often exhibit characteristic dynamics near a tipping point to a new dynamical regime. For instance, critical slowing down -- the tendency for a system to start `wobbling'-- can increase close to a tipping point. We used a linear support vector machine to classify the sentiment of geo-located United States and California tweets concern...

Talk Prof. Peer Fischer 06-07-2017 Micro Nano and Molecular Systems Lab: New Devices and Technologies This talk will look at hardware-based means of assembling, controlling and driving systems at the smallest of scales, including those that can become autonomous. I will show that insights from physics, chemistry and material engineering can be used to permit the simplification and miniaturization of otherwise bulky systems and that this can give rise to new technologies. One of the technologies we have invented may also permit the development of new imaging devices. Jane Walters Julia Braun

Talk Anastasia Pentina 05-07-2017 Multi-task Learning with Labeled and Unlabeled Tasks In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, that required that annotated training data must be available for all tasks, I will talk about a new setting, in which for some tasks, potentially most of them, only unlabeled training data is available. Consequently, to solve all tasks, information must be transfered between tasks with labels and tasks without labels. Focussing on an instance-based transfer method I will consider two variants of this setting: when the set of labeled tasks i... Georg Martius

Probabilistic Numerics Talk Toni Karvonen 04-07-2017 Some parallels between classical and kernel quadrature This talk draws three parallels between classical algebraic quadrature rules, that are exact for polynomials of low degree, and kernel (or Bayesian) quadrature rules: i) Computational efficiency. Construction of scalable multivariate algebraic quadrature rules is challenging whereas kernel quadrature necessitates solving a linear system of equations, quickly becoming computationally prohibitive. Fully symmetric sets and Smolyak sparse grids can be used to solve both problems. ii) Derivatives and optimal rules. Algebraic degree of a Gaussian quadrature rule cannot be improved by adding deriv... Alexandra Gessner

Empirical Inference IS Colloquium Frederick Eberhardt 03-07-2017 Causal Macro Variables 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 meth... Sebastian Weichwald

Talk Christoph Lampert 30-06-2017 Recent Projects on Lifelong Learning Georg Martius

Autonomous Motion Talk Omur Arslan 27-06-2017 Motion Planning via Reference Governors: Towards Closing the Gap Between High-Level and Low-Level Motion Planning In robotics, it is often practically and theoretically convenient to design motion planners for approximate simple robot and environment models first, and then adapt such reference planners to more accurate complex settings. In this talk, I will introduce a new approach to extend the applicability of motion planners of simple settings to more complex settings using reference governors. Reference governors are add-on control schemes for closed-loop dynamical systems to enforce constraint satisfaction while maintaining stability, and offers a systematic way of separating the issues of stabili... Stefan Schaal Lidia Pavel

Autonomous Motion Talk Sarah Bechtle 27-06-2017 On the Sense of Agency and of Object Permanence in Robots This work investigates the development of the sense of agency and of object permanence in humanoid robots. Based on findings from developmental psychology and from neuroscience, development of sense of object permanence is linked to development of sense of agency and to processes of internal simulation of sensor activity. In the course of the work, two sets of experiments will be presented, in the first set a humanoid robot has to learn the forward relationship between its movements and their sensory consequences perceived from the visual input. In particular, a self-monitoring mechanism w... Stefan Schaal Lidia Pavel

Perceiving Systems Talk Seong Joon Oh 22-06-2017 From understanding to controlling privacy against automatic person identification Growth of the internet and social media has spurred the sharing and dissemination of personal data at large scale. At the same time, recent developments in computer vision has enabled unseen effectiveness and efficiency in automated recognition. It is clear that visual data contains private information that can be mined, yet the privacy implications of sharing such data have been less studied in computer vision community. In the talk, I will present some key results from our study of the implications of the development of computer vision on the identifiability in social media, and an analys... Siyu Tang

Autonomous Vision Talk Matthias Niessner 14-06-2017 Reconstructing and Understanding 3D Indoor Environments In the recent years, commodity 3D sensors have become easily and widely available. These advances in sensing technology have spawned significant interest in using captured 3D data for mapping and semantic understanding of 3D environments. In this talk, I will give an overview of our latest research in the context of 3D reconstruction of indoor environments. I will further talk about the use of 3D data in the context of modern machine learning techniques. Specifically, I will highlight the importance of training data, and how can we efficiently obtain labeled and self-supervised ground truth... Despoina Paschalidou

Probabilistic Numerics Talk Jon Cockayne 13-06-2017 Bayesian Probabilistic Numerical Methods The emergent field of probabilistic numerics has thus far lacked rigorous statistical foundations. We establish that a class of Bayesian probabilistic numerical methods can be cast as the solution to certain non-standard Bayesian inverse problems. This allows us to establish general conditions under which Bayesian probabilistic numerical methods are well-defined, encompassing both non-linear models and non-Gaussian prior distributions. For general computation, a numerical approximation scheme is developed and its asymptotic convergence is established. The theoretical development is then ext... Michael Schober

Empirical Inference Talk Felix Leibfried and Jordi Grau-Moya 13-06-2017 Model-based reinforcement learning for sequential decision-making Autonomous systems rely on learning from experience to automatically refine their strategy and adapt to their environment, and thereby have huge advantages over traditional hand engineered systems. At PROWLER.io we use reinforcement learning (RL) for sequential decision making under uncertainty to develop intelligent agents capable of acting in dynamic and unknown environments. In this talk we first give a general overview of the goals and the research conducted at PROWLER.io. Then, we will talk about two specific research topics. The first is Information-Theoretic Model Uncertainty which d... Michel Besserve

Perceiving Systems Talk Nadine Rüegg 06-06-2017 From Camera Synchronization to Deep Learning We transfer a monocular motion stereo 3D reconstruction algorithm from a mobile device (Google Project Tango Tablet) to a rigidly mounted external camera of higher image resolution. A reliable camera synchronization is crucial for the usability of the tablets IMU data and thus a time synchronization method developed. It is based on the joint movement of the cameras. In a second project, we move from outdoor video scenes to aerial images and strive to segment them into polygonal shapes. While most existing approaches address the problem of automated generation of online maps as a pixel-wise... Siyu Tang

Talk Alexey Dosovitskiy 06-06-2017 Learning to see and act in dynamic three-dimensional environments Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration. Lars Mescheder Aseem Behl

Autonomous Vision Talk Alexey Dosovitskiy 06-06-2017 Learning to see and act in dynamic three-dimensional environments Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration. Lars Mescheder Aseem Behl

Autonomous Vision Event 01-06-2017 - 02-06-2017 AVG Spring Retreat

Perceiving Systems Talk Partha Ghosh 31-05-2017 Human Motion Models We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or mo- tion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during train- ing time. This Dropout Autoe... Gerard Pons-Moll

Talk Endri Dibra 30-05-2017 3D shape from monocular images with data-driven priors Estimating 3D shape from monocular 2D images is a challenging and ill-posed problem. Some of these challenges can be alleviated if 3D shape priors are taken into account. In the field of human body shape estimation, research has shown that accurate 3D body estimations can be achieved through optimization, by minimizing error functions on image cues, such as e.g. the silhouette. These methods though, tend to be slow and typically require manual interactions (e.g. for pose estimation). In this talk, we present some recent works that try to overcome such limitations, achieving interactive rate... Gerard Pons-Moll

Perceiving Systems Talk Endri Dibra 30-05-2017 3D shape from monocular images with data-driven priors Estimating 3D shape from monocular 2D images is a challenging and ill-posed problem. Some of these challenges can be alleviated if 3D shape priors are taken into account. In the field of human body shape estimation, research has shown that accurate 3D body estimations can be achieved through optimization, by minimizing error functions on image cues, such as e.g. the silhouette. These methods though, tend to be slow and typically require manual interactions (e.g. for pose estimation). In this talk, we present some recent works that try to overcome such limitations, achieving interactive rate... Gerard Pons-Moll

Empirical Inference IS Colloquium Sebastian Nowozin 29-05-2017 Probabilistic Deep Learning: From Density Estimation to Representation Learning Probabilistic deep learning methods have recently made great progress for generative and discriminative modeling. I will give a brief overview of recent developments and then present two contributions. The first is on a generalization of generative adversarial networks (GAN), extending their use considerably. GANs can be shown to approximately minimize the Jensen-Shannon divergence between two distributions, the true sampling distribution and the model distribution. We extend GANs to the class of f-divergences which include popular divergences such as the Kullback-Leibler diver... Lars Mescheder

Perceiving Systems Talk Sven Dickinson 29-05-2017 The Perceptual Advantage of Symmetry for Scene Perception Human observers can classify photographs of real-world scenes after only a very brief exposure to the image (Potter & Levy, 1969; Thorpe, Fize, Marlot, et al., 1996; VanRullen & Thorpe, 2001). Line drawings of natural scenes have been shown to capture essential structural information required for successful scene categorization (Walther et al., 2011). Here, we investigate how the spatial relationships between lines and line segments in the line drawings affect scene classification. In one experiment, we tested the effect of removing either the junctions or the middle segments between juncti... Ahmed Osman

Perceiving Systems Talk Yael Moses 24-05-2017 Dynamic Scene Analysis Using CrowdCam Data Dynamic events such as family gatherings, concerts or sports events are often photographed by a group of people. The set of still images obtained this way is rich in dynamic content. We consider the question of whether such a set of still images, rather the traditional video sequences, can be used for analyzing the dynamic content of the scene. This talk will describe several instances of this problem, their solutions and directions for future studies. In particular, we will present a method to extend epipolar geometry to predict location of a moving feature in CrowdCam images. The method ... Jonas Wulff

Talk Guido Montúfar 24-05-2017 Geometry of Neural Networks Deep Learning is one of the most successful machine learning approaches to artificial intelligence. In this talk I discuss the geometry of neural networks as a way to study the success of Deep Learning at a mathematical level and to develop a theoretical basis for making further advances, especially in situations with limited amounts of data and challenging problems in reinforcement learning. I present a few recent results on the representational power of neural networks and then demonstrate how to align this with structures from perception-action problems in order to obtain more efficient ... Jane Walters

Probabilistic Numerics Talk Dino Sejdinovic 22-05-2017 Inference with Kernel Embeddings Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric hypothesis testing and for learning on distributional inputs. I will give an overview of this framework and present some of its recent applications within the context of approximate Bayesian inference. Further, I will discuss a recent modification of MMD which aims to encode invariance to additive symmetric noise and leads to learning on distributions robust to the distributional covariate shift, e.g. where measurement noise on the... Philipp Hennig

Autonomous Motion Talk Dr. Raj Madhavan 19-05-2017 Humanitarian Technologies & Technology-Public Policy Considerations for Societal Good Many of the existing Robotics & Automation (R&A) technologies are at a sufficient level of maturity and are widely accepted by the academic (and to a lesser extent by the industrial) community after having undergone the scientific rigor and peer reviews that accompany such works. I believe that most of the past and current research and development efforts in robotics and automation have been squarely aimed at increasing the Standard of Living (SoL) in developed economies where housing, running water, transportation, schools, access to healthcare, to name a few, are taken for granted... Ludovic Righetti

Perceiving Systems Talk Cordelia Schmid 19-05-2017 Learning to segment moving objects This talk addresses the task of segmenting moving objects in unconstrained videos. We introduce a novel two-stream neural network with an explicit memory module to achieve this. The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. Given video frames as input, our approach... Osman Ulusoy

Max Planck Lecture Robert J. Birgeneau 18-05-2017 Superconductors Old and New Solid State Physics is a field which continuously renews itself through the discovery of new materials and new phenomena. This has been particularly true for the subfield of superconductivity.

Autonomous Vision Talk Carolin Schmitt 11-05-2017 Biquadratic Forms and Semi-Definite Relaxations I'll present my master thesis "Biquadratic Forms and Semi-Definite Relaxations". It is about biquadratic optimization programs (which are NP-hard generally) and examines a condition under which there exists an algorithm that finds a solution to every instance of the problem in polynomial time. I'll present a counterexample for which this is not possible generally and face the question of what happens if further knowledge about the variables over which we optimise is applied. Fatma Güney

Perceiving Systems Talk Björn Andres 08-05-2017 Graph Decomposition Problems in Image Analysis A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. This talk is about optimization problems whose feasible solutions define decompositions of a graph. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows applications of this problem and proposed generalizations to diverse image analysis tasks. It sk... Christoph Lassner

Talk Rahul Chaudhari and David Gueorguiev 05-05-2017 Haptic Texture Compression and Perceptual Quality Evaluation / Understanding Friction Based Haptic Feedback Colloquium on haptics: Two guests of the department "Haptic Intelligence" (Dept. Kuchenbecker), will each give a short talk this Friday (May 5) in Tübingen. The talks will be broadcasted to Stuttgart, room 2 P4.

Perceiving Systems Talk Gul Varol 04-05-2017 Learning from Synthetic Humans Estimating human pose, shape, and motion from images and video are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL: a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 m... Dimitris Tzionas

Autonomous Motion Talk Sylvain Calinon 27-04-2017 Robot learning from few demonstrations by exploiting the structure and geometry of data Human-centric robotic applications often require the robots to learn new skills by interacting with the end-users. From a machine learning perspective, the challenge is to acquire skills from only few interactions, with strong generalization demands. It requires: 1) the development of intuitive active learning interfaces to acquire meaningful demonstrations; 2) the development of models that can exploit the structure and geometry of the acquired data in an efficient way; 3) the development of adaptive control techniques that can exploit the learned task variations and coordination patterns.... Ludovic Righetti

Autonomous Motion Talk Dr. Andrea Del Prete 25-04-2017 Multi-contact locomotion control for legged robots This talk will survey recent work to achieve multi-contact locomotion control of humanoid and legged robots. I will start by presenting some results on robust optimization-based control. We exploited robust optimization techniques, either stochastic or worst-case, to improve the robustness of Task-Space Inverse Dynamics (TSID), a well-known control framework for legged robots. We modeled uncertainties in the joint torques, and we immunized the constraints of the system to any of the realizations of these uncertainties. We also applied the same methodology to ensure the balance of the robot ... Ludovic Righetti

Probabilistic Numerics Talk Philipp Berens 24-04-2017 Towards a complete parts list: multimodal data science in the retina The retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. I will review our recent contributions in this area, showing how analyzing multimodal datasets from electron m... Philipp Hennig

Perceiving Systems Talk Yanxi Liu 13-04-2017 Dancing with TURKs or Tai Chi with a Master? From gait, dance to martial art, human movements provide rich, complex yet coherent spatiotemporal patterns reflecting characteristics of a group or an individual. We develop computer algorithms to automatically learn such quality discriminative features from multimodal data. In this talk, I present a trilogy on learning from human movements: (1) Gait analysis from video data: based on frieze patterns (7 frieze groups), a video sequence of silhouettes is mapped into a pair of spatiotemporal patterns that are near-periodic along the time axis. A group theoretical analysis of periodic pat... Laura Sevilla Siyu Tang

Perceiving Systems Talk Silvia Zuffi 07-04-2017 Building Multi-Family Animal Models 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 ...

Talk Moritz Hardt, Google Brain / University of California, Berkeley 04-04-2017 Discovering discrimination in supervised learning Moritz Hardt will review some progress and challenges towards preventing discrimination based on sensitive attributes in supervised learning. Michael Black Stefan Schaal Bernhard Schölkopf

Autonomous Motion Symposium 27-03-2017 - 29-03-2017 Interactive Multisensory Object Perception for Embodied Agents Symposium at the AAAI Spring Symposium Series in 2017 at Stanford University. Jeannette Bohg

Autonomous Motion Talk Todor Stoyanov and Robert Krug 16-03-2017 Integrated Perception and Control for Autonomous Manipulation In this talk we will give an overview of research efforts within autonomous manipulation at the AASS Research Center, Örebro University, Sweden. We intend to give a holistic view on the historically separated subjects of robot motion planning and control. In particular, viewing motion behavior generation as an optimal control problem allows for a unified formulation that is uncluttered by a-priori domain assumptions and simplified solution strategies. Furthermore, We will also discuss the problems of workspace modeling and perception and how to integrate them in the overarching problem o... Ludovic Righetti

Empirical Inference IS Colloquium John Cunningham 06-03-2017 Statistical testing of epiphenomena for multi-index data As large tensor-variate data increasingly become the norm in applied machine learning and statistics, complex analysis methods similarly increase in prevalence. Such a trend offers the opportunity to understand more intricate features of the data that, ostensibly, could not be studied with simpler datasets or simpler methodologies. While promising, these advances are also perilous: these novel analysis techniques do not always consider the possibility that their results are in fact an expected consequence of some simpler, already-known feature of simpler data (for example, treating the te... Philipp Hennig

Autonomous Motion Talk Matteo Turchetta 22-02-2017 Safe Exploration in Finite Markov Decision Processes with Gaussian Processes In classical reinforcement learning agents accept arbitrary short term loss for long term gain when exploring their environment. This is infeasible for safety critical applications such as robotics, where even a single unsafe action may cause system failure or harm the environment. In this work, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an a priori unknown safety constraint that depends on states and actions and satisfies certain regularity conditions expressed via a Gaussian process prior. We develop a novel algo... Sebastian Trimpe

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Events & Talks