Events & Talks

Autonomous Vision Event 06-02-2017 - 09-02-2017 Computer Vision Retreat Andreas Geiger

Autonomous Motion Talk Dr. Thomas Besselmann 13-01-2017 Power meets Computation This is the story of the novel model predictive control (MPC) solution for ABB’s largest drive, the Megadrive LCI. LCI stands for load commutated inverter, a type of current source converter which powers large machineries in many industries such as marine, mining or oil & gas. Starting from a small software project at ABB Corporate Research, this novel control solution turned out to become the first time ever MPC was employed in a 48 MW commercial drive. Subsequently it was commissioned at Kollsnes, a key facility of the natural gas delivery chain, in order to increase the plant’s a... Sebastian Trimpe

Empirical Inference IS Colloquium Fabien Lotte 19-12-2016 Human Learning and Alternative Applications Towards Usable Electroencephalography-based Brain-Computer Interfaces Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns of a user into messages or commands for an interactive application. Such brain activity is typically measured using Electroencephalography (EEG), before being processed and classified by the system. EEG-based BCIs have proven promising for a wide range of applications ranging from communication and control for motor impaired users, to gaming targeted at the general public, real-time mental state monitoring and stroke rehabilitation, to name a few. Despite this promising potential, BCIs are still scarcely...

Symposium 13-12-2016 - 16-12-2016 Special Symposium on Intelligent Systems

Talk Ralf Nagel 12-12-2016 Bayesian Inference for Uncertainty Quantification and Inverse Problems The predictive simulation of engineering systems increasingly rests on the synthesis of physical models and experimental data. In this context, Bayesian inference establishes a framework for quantifying the encountered uncertainties and fusing the available information. A summary and discussion of some recently emerged methods for uncertainty propagation (polynomial chaos expansions) and related MCMC-free techniques for posterior computation (spectral likelihood expansions, optimal transportation theory) is presented. Philipp Hennig

Autonomous Vision Talk Laura Leal-Taixé 08-12-2016 Deep Learning and its Relationship with Time In this talk I am going to present the work we have been doing at the Computer Vision Lab of the Technical University of Munich which started as an attempt to better deal with videos (and therefore the time domain) within neural network architectures. Joel Janai

Perceiving Systems Talk Kathleen Robinette 05-12-2016 Modeling Opportunities for Effective Product Development & Sizing Kathleen is the creator of the well-known CAESAR anthropomorphic dataset and is an expert on body shape and apparel fit. Javier Romero

Autonomous Motion Talk Wallace M. Bessa 01-12-2016 - 01-11-2016 Intelligent control of uncertain underactuated mechanical systems Underactuated mechanical systems (UMS) play an essential role in several branches of industrial activity and their application scope ranges from robotic manipulators and overhead cranes to aerospace vehicles and watercrafts. Despite this broad spectrum of applications, the problem of designing accurate controllers for underactuated systems is, however, much more tricky than for fully actuated ones. Moreover, the dynamic behavior of an UMS is frequently uncertain and highly nonlinear, which in fact makes the design of control schemes for such systems a challenge for conventio... Sebastian Trimpe

Autonomous Vision Talk Carsten Rother 21-11-2016 A Collection of Recent Work: From 6D Pose estimation via MRF-Diversity to zebrafish detection In this talk I will present the portfolio of work we conduct in our lab. Herby, I will present three recent body of work in more detail. This is firstly our work on learning 6D Object Pose estimation and Camera localizing from RGB or RGBD images. I will show that by utilizing the concepts of uncertainty and learning to score hypothesis, we can improve the state of the art. Secondly, I will present a new approach for inferring multiple diverse labeling in a graphical model. Besides guarantees of an exact solution, our method is also faster than existing techniques. Finally, I will present a ... Aseem Behl

Autonomous Vision Talk Bogdan Savchynskyy 21-11-2016 Future of graphical models: more modeling power, parallelization, scalable solvers We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future. Aseem Behl

Talk Dr. Bogdan Savchynskyy 21-11-2016 Future of graphical models: more modeling power, parallelization, scalable solvers. We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future. Aseem Behl

Perceiving Systems Talk Hedvig Kjellström 27-10-2016 Factorized Latent Representations for Improved Automated Diagnostics In this talk I will first outline my different research projects. I will then focus on one project with applications in Health, and introduce the Inter-Battery Topic Model (IBTM). Our approach extends traditional topic models by learning a factorized latent variable representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provided by representing data in ter...

Autonomous Motion Talk Chris Atkeson and Akihiko Yamaguchi 19-10-2016 Optical Robot Skin and Whole Body Vision Chris Atkeson will talk about the motivation for optical robot skin and whole-body vision. Akihiko Yamaguchi will talk about a first application, FingerVision. Ludovic Righetti

Talk Jean-Claude Passy 29-09-2016 Numerics in Computational Stellar Astrophysics The importance of computer science in astrophysical research has increased tremendously over the past 15 years. Indeed, as observational facilities and missions are constantly pushing their precision limit, theorists need to provide observers with more and more realistic numerical models. These models need to be verified, validated, and their uncertainties must be assessed. In this talk, I will present the results of two independent numerical studies aiming at solving some fundamental problems in stellar astrophysics. First, I will explain how we have used different 3D hydrodynamics codes t... Raffi Enficiaud

Autonomous Motion Talk Jose R. Medina 27-09-2016 Considering uncertainty in robot decision-making: control and modelling aspects Control under uncertainty is an omnipresent problem in robotics that typically arises when robots must cope with unknown environments/tasks. Robot control typically ignores uncertainty by considering only the expected outcomes of the robot’s internal model. Interestingly, neuroscientist have shown that humans adapt their decisions depending on the level of uncertainty which is not reflected in the expected values, but in higher order statistics. In this talk I will first present an approach to systematically address this problem in the context of stochastic optimal control. I will then giv... Ludovic Righetti

Autonomous Motion Talk Stéphane Caron 19-09-2016 Multi-contact Stability: Support Areas and Volumes for Humanoid Locomotion under Frictional Contacts Humanoid locomotion on horizontal floors was solved by closing the feedback loop on the Zero-tiling Moment Point (ZMP), a measurable dynamic point that needs to stay inside the foot contact area to prevent the robot from falling (contact stability criterion). However, this criterion does not apply to general multi-contact settings, the "new frontier" in humanoid locomotion. In this talk, we will see how the ideas of ZMP and support area can be generalized and applied to multi-contact locomotion. First, we will show how support areas can be calculated in any virtual plane, allowin... Ludovic Righetti

Perceiving Systems Talk Siyu Tang 25-08-2016 Graph decomposition for multi-person tracking, pose estimation and motion segmentation Understanding people in images and videos is a problem studied intensively in computer vision. While continuous progress has been made, occlusions, cluttered background, complex poses and large variety of appearance remain challenging, especially for crowded scenes. In this talk, I will explore the algorithms and tools that enable computer to interpret people's position, motion and articulated poses in the real-world challenging images and videos.More specifically, I will discuss an optimization problem whose feasible solutions define a decomposition of a given graph. I will highlight the a... Naureen Mahmood

Perceiving Systems Talk Dimitris Tzionas 04-08-2016 Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with st... Javier Romero

Autonomous Vision Talk Anton Milan 22-07-2016 Bipartite Matching and Multi-target Tracking Matching between two sets arises in various areas in computer vision, such as feature point matching for 3D reconstruction, person re-identification for surveillance or data association for multi-target tracking. Most previous work focused either on designing suitable features and matching cost functions, or on developing faster and more accurate solvers for quadratic or higher-order problems. In the first part of my talk, I will present a strategy for improving state-of-the-art solutions by efficiently computing the marginals of the joint matching probability. The second part of my talk wi...

Event 11-07-2016 - 13-07-2016 CLS Workshop - Deep Learning: Theory and Practice Workshop in Donaueschingen Peter Vincent Gehler

Event 18-06-2016 Open House - Tag der offenen Tür - Max Planck Campus Tübingen The four institutions on Tübingen Max Planck Campus open their doors to the interested public. Claudia Daefler

Perceiving Systems Talk Timo Bolkart 09-06-2016 Dynamic and Groupwise Statistical Analysis of 3D Faces The accurate reconstruction of facial shape is important for applications such as telepresence and gaming. It can be solved efficiently with the help of statistical shape models that constrain the shape of the reconstruction. In this talk, several methods to statistically analyze static and dynamic 3D face data are discussed. When statistically analyzing faces, various challenges arise from noisy, corrupt, or incomplete data. To overcome the limitations imposed by the poor data quality, we leverage redundancy in the data for shape processing. This is done by processing entire motion seq...

Autonomous Motion Talk Christian Ebenbauer 08-06-2016 Extremum Seeking Control: Theory and Applications in Multi-Agent Systems In many control applications it is the goal to operate a dynamical system in an optimal way with respect to a certain performance criterion. In a combustion engine, for example, the goal could be to control the engine such that the emissions are minimized. Due to the complexity of an engine, the desired operating point is unknown or may even change over time so that it cannot be determined a priori. Extremum seeking control is a learning-control methodology to solve such kind of control problems. It is a model-free method that optimizes the steady-state behavior of a dynamical syste... Sebastian Trimpe

Max Planck Lecture Professor Naomi Ehrich Leonard 06-06-2016 On the Nonlinear Dynamics of Collective Decision-Making in Nature and Design The successful deployment of complex, multi-agent systems requires well-designed, agent-level control strategies that accommodate sensing, communication, and computational limitations on individual agents.

Talk Georg Martius 31-05-2016 Self-organization of behavior in autonomous robot development 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 t... Jane Walters

Perceiving Systems Talk Cordelia Schmid 21-04-2016 Pose-based human action recognition. 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-le...

Perceiving Systems Talk Gül Varol 12-04-2016 Long-term Temporal Convolutions for Action Recognition 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 ...

Perceiving Systems Talk Helge Rhodin 08-04-2016 Ray Tracing for Computer Vision 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 demon...

Perceiving Systems Talk Aamir Ahmad 05-04-2016 Multirobot Cooperative State Estimation - towards Scalability and Active Perception 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...

Perceiving Systems Talk Valsamis Ntouskos 04-04-2016 Regularization and Statistical Inverse Problems in Shape and Motion Modeling 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 anim...

Optics and Sensing Laboratory Talk Eric Price 10-03-2016 Computer Vision on UAVs – practical considerations 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 du... Alina Allakhverdieva

Perceiving Systems Talk Lars Mescheder 03-03-2016 From image restoration to image understanding 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 missi...

Empirical Inference Talk Sascha Quantz 01-03-2016 Images of planets orbiting other stars 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 detectio...

Perceiving Systems Talk Helga Griffiths 24-02-2016 Interaction of Science and Art 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. Emma-Jayne Holderness

Autonomous Motion Talk Felix Berkenkamp 23-02-2016 Safe Bayesian Optimization: From Safe Parameter Optimization to the Exploration of Unknown Environments 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... Sebastian Trimpe

Empirical Inference IS Colloquium Aldo Faisal 25-01-2016 What can we learn if we record the complete perception and action of a person? 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... Matthias Hohmann

Autonomous Motion Talk Jun Nakanishi 01-12-2015 Motor Learning and Control from Dynamical Systems Based Approach and Optimal Control 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... Ludovic Righetti

Event 30-11-2015 Max Planck ETH Center for Learning Systems Inauguration Learning systems are part of our everyday life, either as software in internet search as well as in image recognition or as physical systems such as robot vacuum cleaners and autonomous vehicles. The Max Planck ETH Center for Learning Systems promotes leading experts as well as junior scientists in the interdisciplinary, pioneering research field of learning systems. It establishes a centre of excellence offering a unique platform for scientific networking and research into novel technologies at the science and technology hub in Baden-Wuerttemberg and Switzerland. Jane Walters Matthias Tröndle Claudia Daefler

Autonomous Motion Talk Alexander Sprowitz 23-11-2015 Control and design of very dynamic legged robots inspired from biomechanical and neurocontrol studies The current performance gap between legged animals and legged robots is large. Animals can reach high locomotion speed in complex terrain, or run at a low cost of transport. They are able to rapidly sense their environment, process sensor data, learn and plan locomotion strategies, and execute feedforward and feedback controlled locomotion patterns fluently on the fly. Animals use hardware that has, compared to the latest man-made actuators, electronics, and processors, relatively low bandwidth, medium power density, and low speed. The most common approach to legged robot locomotion... Ludovic Righetti

Autonomous Motion IS Colloquium Prof. Pieter Abbeel 13-11-2015 Making Robots Learn Programming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what often ends up being time-consuming task specific programming. In this talk I will describe the ideas behind two promising types of robot learning: First I will discuss apprenticeship learning, in which robots learn from human demonstrations, and which has enabled autonomous helicopter aerobatics, knot tying, basic suturing, and cloth manipulation. Then I will discuss deep reinforcement learning, in which robots learn through their own trial and error, and which has ... Stefan Schaal

Workshop 11-11-2015 - 13-11-2015 Learning Control Workshop (Max Planck ETH Center for Learning Systems) We are pleased to announce the First Max Planck ETH Workshop on Learning Control within the Max Planck ETH Center for Learning Systems. The workshop will take place November 11-13 2015 at the Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen. We cordially invite all researchers from ETH Zürich and MPI-IS interested in the area of Learning Control to participate and actively contribute to this workshop. Sebastian Trimpe Ludovic Righetti Melanie Zeilinger

Talk Prof. David W. Jacobs 10-11-2015 Understanding Plants and Animals I will describe a series of work that aims to automatically understand images of animals and plants. I will begin by describing recent work that uses Bounded Distortion matching to model pose variation in animals. Using a generic 3D model of an animal and multiple images of different individuals in various poses, we construct a model that captures the way in which the animal articulates. This is done by solving for the pose of the template that matches each image while simultaneously solving for the stiffness of each tetrahedron of the model. We minimize an L1 norm on stiffness, produci... Stephan Streuber

Perceiving Systems Talk Prof. David W. Jacobs 10-11-2015 Understanding Plants and Animals I will describe a series of work that aims to automatically understand images of animals and plants. I will begin by describing recent work that uses Bounded Distortion matching to model pose variation in animals. Using a generic 3D model of an animal and multiple images of different individuals in various poses, we construct a model that captures the way in which the animal articulates. This is done by solving for the pose of the template that matches each image while simultaneously solving for the stiffness of each tetrahedron of the model. We minimize an L1 norm on stiffness, produci... Stephan Streuber

Perceiving Systems Talk Olga Diamanti 28-10-2015 Design of Tangent Vector-Set Fields using Polynomials The design of tangent vector fields on discrete surfaces is a basic building block for many geometry processing applications, such as surface remeshing, parameterization and architectural geometric design. Many applications require the design of multiple vector fields (vector sets) coupled in a nontrivial way; for example, sets of more than two vectors are used for meshing of triangular, quadrilateral and hexagonal meshes. In this talk, a new, polynomial-based representation for general unordered vector sets will be presented. Using this representation we can efficiently interpolate user pr... Gerard Pons-Moll

Empirical Inference IS Colloquium Jonas Richiardi 19-10-2015 Imaging genomics of functional brain networks During rest, brain activity is intrinsically synchronized between different brain regions, forming networks of coherent activity. These functional networks (FNs), consisting of multiple regions widely distributed across lobes and hemispheres, appear to be a fundamental theme of neural organization in mammalian brains. Despite hundreds of studies detailing this phenomenon, the genetic and molecular mechanisms supporting these functional networks remain undefined. Previous work has mostly focused on polymorphisms in candidate genes, or used a twin study approach to demonstrate heritability... Moritz Grosse-Wentrup Michel Besserve

Perceiving Systems Talk Max Welling 19-10-2015 Learning to generate The recent amazing success of deep learning has been mainly in discriminative learning, that is, classification and regression. An important factor for this success has been, besides Moore's law, the availability of large labeled datasets. However, it is not clear whether in the future the amount of available labels grows as fast as the amount of unlabeled data, providing one argument to be interested in unsupervised and semi-supervised learning. Besides this there are a number of other reasons why unsupervised learning is still important, such as the fact that data in the life sciences ... Peter Vincent Gehler

IS Colloquium Vladimir Vapnik 12-10-2015 Intelligent Learning Michel Besserve

Autonomous Motion Talk Yasemin Bekiroglu 12-10-2015 Shape and Stability Estimation based on Learning from Visual and Tactile Data Unknown information required to plan grasps such as object shape and pose needs to be extracted from the environment through sensors. However, sensory measurements are noisy and associated with a degree of uncertainty. Furthermore, object parameters relevant to grasp planning may not be accurately estimated, e.g., friction and mass. In real-world settings, these issues can lead to grasp failures with serious consequences. I will talk about learning approaches using real sensory data, e.g., visual and tactile, to assess grasp success (discriminative and generative) that can be used to trigge... Jeannette Bohg

Empirical Inference Talk Gael Varoquaux 09-10-2015 Using machine learning to understand cognition from brain images Moritz Grosse-Wentrup

Talk Robin Evans 29-09-2015 Causal Models and How to Refute Them Directed acyclic graph models (DAG models, also called Bayesian networks) are widely used in the context of causal inference, and they can be manipulated to represent the consequences of intervention in a causal system. However, DAGs cannot fully represent causal models with confounding; other classes of graphs, such as ancestral graphs and ADMGs, have been introduced to deal with this using additional kinds of edge, but we show that these are not sufficiently rich to capture the range of possible models. In fact, no mixed graph over the observed variables is rich enough, regardless of how ... Sabrina Rehbaum

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