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Institute Talks

Digital Humans At Disney Research

IS Colloquium
  • 25 May 2018 • 11:00 12:00
  • Thabo Beeler
  • MPI-IS lecture hall (N0.002)

Disney Research has been actively pushing the state-of-the-art in digitizing humans over the past decade, impacting both academia and industry. In this talk I will give an overview of a selected few projects in this area, from research into production. I will be talking about photogrammetric shape acquisition and dense performance capture for faces, eye and teeth scanning and parameterization, as well as physically based capture and modelling for hair and volumetric tissues.

Organizers: Timo Bolkart

Learning dynamical systems using SMC

IS Colloquium
  • 28 May 2018 • 11:15 12:15
  • Thomas Schön
  • MPI-IS lecture hall (N0.002)

Abstract: Sequential Monte Carlo (SMC) methods (including the particle filters and smoothers) allows us to compute probabilistic representations of the unknown objects in models used to represent for example nonlinear dynamical systems. This talk has three connected parts: 1. A (hopefully pedagogical) introduction to probabilistic modelling of dynamical systems and an explanation of the SMC method. 2. In learning unknown parameters appearing in nonlinear state-space models using maximum likelihood it is natural to make use of SMC to compute unbiased estimates of the intractable likelihood. The challenge is that the resulting optimization problem is stochastic, which recently inspired us to construct a new solution to this problem. 3. A challenge with the above (and in fact with most use of SMC) is that it all quickly becomes very technical. This is indeed the key challenging in spreading the use of SMC methods to a wider group of users. At the same time there are many researchers who would benefit a lot from having access to these methods in their daily work and for those of us already working with them it is essential to reduce the amount of time spent on new problems. We believe that the solution to this can be provided by probabilistic programming. We are currently developing a new probabilistic programming language that we call Birch. A pre-release is available from birch-lang.org/ It allow users to use SMC methods without having to implement the algorithms on their own.

Organizers: Philipp Hennig

Making Sense of the Physical World with High-Resolution Tactile Sensing

Talk
  • 05 June 2018 • 11:00 12:00
  • Wenzhen Yuan
  • MPI-IS Stuttgart, Heisenbergstr. 3, Room 2P4

Why cannot the current robots act intelligently in the real-world environment? A major challenge lies in the lack of adequate tactile sensing technologies. Robots need tactile sensing to understand the physical environment, and detect the contact states during manipulation. Progress requires advances in the sensing hardware, but also advances in the software that can exploit the tactile signals. We developed a high-resolution tactile sensor, GelSight, which measures the geometry and traction field of the contact surface. For interpreting the high-resolution tactile signal, we utilize both traditional statistical models and deep neural networks. I will describe my research on both exploration and manipulation. For exploration, I use active touch to estimate the physical properties of the objects. The work has included learning the hardness of artificial objects, as well as estimating the general properties of natural objects via autonomous tactile exploration. For manipulation, I study the robot’s ability to detect slip or incipient slip with tactile sensing during grasping. The research helps robots to better understand and flexibly interact with the physical world.

Organizers: Katherine Kuchenbecker

Biomechanical insights into flexible wings from gliding mammals

Talk
  • 08 June 2018 • 11:00 12:00
  • Dr. Greg Byrnes
  • Room 3P02 - Stuttgart

Gliding evolved at least nine times in mammals. Despite the abundance and diversity of gliding mammals, little is known about their convergent morphology and mechanisms of aerodynamic control. Many gliding animals are capable of impressive and agile aerial behaviors and their flight performance depends on the aerodynamic forces resulting from airflow interacting with a flexible, membranous wing (patagium). Although the mechanisms that gliders use to control dynamic flight are poorly understood, the shape of the gliding membrane (e.g., angle of attack, camber) is likely a primary factor governing the control of the interaction between aerodynamic forces and the animal’s body. Data from field studies of gliding behavior, lab experiments examining membrane shape changes during glides and morphological and materials testing data of gliding membranes will be presented that can aid our understanding of the mechanisms gliding mammals use to control their membranous wings and potentially provide insights into the design of man-made flexible wings.

Organizers: Metin Sitti Ardian Jusufi

Learning Control for Intelligent Physical Systems

Talk
  • 13 July 2018 • 14:15 14:45
  • Dr. Sebastian Trimpe
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

Modern technology allows us to collect, process, and share more data than ever before. This data revolution opens up new ways to design control and learning algorithms, which will form the algorithmic foundation for future intelligent systems that shall act autonomously in the physical world. Starting from a discussion of the special challenges when combining machine learning and control, I will present some of our recent research in this exciting area. Using the example of the Apollo robot learning to balance a stick in its hand, I will explain how intelligent agents can learn new behavior from just a few experimental trails. I will also discuss the need for theoretical guarantees in learning-based control, and how we can obtain them by combining learning and control theory.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

Household Assistants: the Path from the Care-o-bot Vision to First Products

Talk
  • 13 July 2018 • 14:45 15:15
  • Dr. Martin Hägele
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

In 1995 Fraunhofer IPA embarked on a mission towards designing a personal robot assistant for everyday tasks. In the following years Care-O-bot developed into a long-term experiment for exploring and demonstrating new robot technologies and future product visions. The recent fourth generation of the Care-O-bot, introduced in 2014 aimed at designing an integrated system which addressed a number of innovations such as modularity, “low-cost” by making use of new manufacturing processes, and advanced human-user interaction. Some 15 systems were built and the intellectual property (IP) generated by over 20 years of research was recently licensed to a start-up. The presentation will review the path from an experimental platform for building up expertise in various robotic disciplines to recent pilot applications based on the now commercial Care-O-bot hardware.

Organizers: Katherine Kuchenbecker Ildikó Papp-Wiedmann Matthias Tröndle Claudia Daefler

Brain-machine interfaces: New treatment options for psychiatric disorders

IS Colloquium
  • 06 February 2017 • 11:15 12:15
  • Surjo R. Soekadar

Organizers: Moritz Grosse-Wentrup


Power meets Computation

Talk
  • 13 January 2017 • 11:00 12:30
  • Dr. Thomas Besselmann
  • AMD seminar room (PES 15)

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 availability. In this presentation I will talk about the magic behind this success story, the so-called Embedded MPC algorithms, and my objective will be to demonstrate the possibilities when power meets computation.

Organizers: Sebastian Trimpe


  • Fabien Lotte
  • Max Planck House Lecture Hall

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 used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low robustness and reliability, as well as their long training times. In this talk I present some of our research aimed at addressing these points in order to make EEG-based BCIs usable, i.e., to increase their efficacy and efficiency. In particular, I will present a set of contributions towards this goal 1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and 2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful, e.g., for neuroergonomics or real-time brain activity and mental state visualization.


  • Ralf Nagel
  • AGBS Seminar Room

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.

Organizers: Philipp Hennig


Deep Learning and its Relationship with Time

Talk
  • 08 December 2016 • 11:00 12:00
  • Laura Leal-Taixé
  • MRZ Seminar Room

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.

Organizers: Joel Janai


  • Kathleen Robinette
  • MRZ Seminar Room

Kathleen is the creator of the well-known CAESAR anthropomorphic dataset and is an expert on body shape and apparel fit.

Organizers: Javier Romero


Intelligent control of uncertain underactuated mechanical systems

Talk
  • 01 December 2016 • 11:00 - 01 November 2016 • 12:00
  • Wallace M. Bessa
  • AMD Seminar Room (Paul-Ehrlich-Str. 15, 1rst floor)

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 conventional and well established methods. In this talk, it will be shown that intelligent algorithms, such as fuzzy logic and artificial neural networks, could be combined with nonlinear control techniques (feedback linearization or sliding modes) in order to improve both set-point regulation and trajectory tracking of uncertain underactuated mechanical systems.

Organizers: Sebastian Trimpe


  • Carsten Rother
  • MRZ seminar room

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 recent work in which we show that popular Auto-context Decision Forests can be mapped to Deep ConvNets for Semantic Segmentation. We use this to detect the spine of a zebrafish, in case when little training data is available.

Organizers: Aseem Behl


  • Dr. Bogdan Savchynskyy
  • MRZ seminar room

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.

Organizers: Aseem Behl


  • Bogdan Savchynskyy
  • Mrz Seminar Room (room no. 0.A.03)

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.

Organizers: Aseem Behl