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Institute Talks

Less-artificial intelligence

Talk
  • 18 June 2018 • 15:00 16:00
  • Prof. Dr. Matthias Bethge
  • MPI-IS Stuttgart - 2R04

Haptic Engineering and Science at Multiple Scales

Talk
  • 20 June 2018 • 11:00 12:00
  • Yon Visell, PhD
  • MPI-IS Stuttgart, Heisenbergstr. 3, Room 2P4

I will describe recent research in my lab on haptics and robotics. It has been a longstanding challenge to realize engineering systems that can match the amazing perceptual and motor feats of biological systems for touch, including the human hand. Some of the difficulties of meeting this objective can be traced to our limited understanding of the mechanics, and to the high dimensionality of the signals, and to the multiple length and time scales - physical regimes - involved. An additional source of richness and complication arises from the sensitive dependence of what we feel on what we do, i.e. on the tight coupling between touch-elicited mechanical signals, object contacts, and actions. I will describe research in my lab that has aimed at addressing these challenges, and will explain how the results are guiding the development of new technologies for haptics, wearable computing, and robotics.

Organizers: Katherine Kuchenbecker

Imitation of Human Motion Planning

Talk
  • 29 June 2018 • 12:00 12:45
  • Jim Mainprice
  • N3.022 (Aquarium)

Humans act upon their environment through motion, the ability to plan their movements is therefore an essential component of their autonomy. In recent decades, motion planning has been widely studied in robotics and computer graphics. Nevertheless robots still fail to achieve human reactivity and coordination. The need for more efficient motion planning algorithms has been present through out my own research on "human-aware" motion planning, which aims to take the surroundings humans explicitly into account. I believe imitation learning is the key to this particular problem as it allows to learn both, new motion skills and predictive models, two capabilities that are at the heart of "human-aware" robots while simultaneously holding the promise of faster and more reactive motion generation. In this talk I will present my work in this direction.

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

The Critical Role of Atoms at Surfaces and Interfaces: Do we really have control? Can we?

Talk
  • 13 July 2018 • 15:45 16:15
  • Prof. Dr. Dawn Bonnell
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

With the ubiquity of catalyzed reactions in manufacturing, the emergence of the device laden internet of things, and global challenges with respect to water and energy, it has never been more important to understand atomic interactions in the functional materials that can provide solutions in these spaces.

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

Interactive Visualization – A Key Discipline for Big Data Analysis

Talk
  • 13 July 2018 • 15:00 15:30
  • Prof. Dr. Thomas Ertl
  • MPI-IS, Stuttgart, Lecture Hall 2 D5

Big Data has become the general term relating to the benefits and threats which result from the huge amount of data collected in all parts of society. While data acquisition, storage and access are relevant technical aspects, the analysis of the collected data turns out to be at the core of the Big Data challenge. Automatic data mining and information retrieval techniques have made much progress but many application scenarios remain in which the human in the loop plays an essential role. Consequently, interactive visualization techniques have become a key discipline of Big Data analysis and the field is reaching out to many new application domains. This talk will give examples from current visualization research projects at the University of Stuttgart demonstrating the thematic breadth of application scenarios and the technical depth of the employed methods. We will cover advances in scientific visualization of fields and particles, visual analytics of document collections and movement patterns as well as cognitive aspects.

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

  • Sarah Bechtle
  • N2.025 (AMD seminar room - 2nd floor)

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 was implemented that allows the robot to distinguish between self-generated movements and those generated by external events. In a second experiment, once having learned this mapping, the self-monitoring mechanism is exploited to suppress the predicted visual consequences of intended movements. The speculation is made that this process can allow for the development of sense of object permanence. It will be shown, that using these predictions, the robot maintains an enhanced simulated image where an object occluded by the movement of the robot arm is still visible, due to sensory attenuation processes.

Organizers: Stefan Schaal Lidia Pavel


  • Omur Arslan
  • N2.025 (AMD seminar room - 2nd floor)

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 stability and constraint enforcement. I will demonstrate example applications of reference governors for sensor-based navigation in environments cluttered with convex obstacles and for smooth extensions of low-order (e.g., position- or velocity-controlled) feedback motion planners to high-order (e.g., force/torque controlled) robot models, while retaining stability and collision avoidance properties.

Organizers: Stefan Schaal Lidia Pavel


  • Seong Joon Oh
  • Aquarium

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 analysis of existing and new anonymisation techniques. In particular, we show that adversarial image perturbations (AIP) introduce human invisible perturbations on the input image that effectively misleads a recogniser. They are far more aesthetic and effective compared to e.g. face blurring. The core limitation, however, is that AIPs are usually generated against specific target recogniser(s), and it is hard to guarantee the performance against uncertain, potentially adaptive recognisers. As a first step towards dealing with the uncertainty, we have introduced a game theoretical framework to obtain the user’s privacy guarantee independent of the randomly chosen recogniser (within some fixed set).

Organizers: Siyu Tang


  • Matthias Niessner
  • PS Seminar Room (N3.022)

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 training datasets from captured 3D content. Finally, I will show a selection of state-of-the-art deep learning approaches, including discriminative semantic labeling of 3D scenes and generative reconstruction techniques.

Organizers: Despoina Paschalidou


  • Felix Leibfried and Jordi Grau-Moya
  • N 4.022 (Seminar Room EI-Dept.)

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 deals with the problem of making robust decisions that take into account unspecified models of the environment. The second is Deep Model-Based Reinforcement Learning which deals with the problem of learning the transition and the reward function of a Markov Decision Process in order to use it for data-efficient learning.

Organizers: Michel Besserve


Bayesian Probabilistic Numerical Methods

Talk
  • 13 June 2017 • 11:00 12:00
  • Jon Cockayne

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 extended to pipelines of numerical computation, wherein several probabilistic numerical methods are composed to perform more challenging numerical tasks. The contribution highlights an important research frontier at the interface of numerical analysis and uncertainty quantification, with some illustrative applications presented.

Organizers: Michael Schober


  • Alexey Dosovitskiy
  • PS Seminar Room (N3.022)

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.

Organizers: Lars Mescheder Aseem Behl


  • Alexey Dosovitskiy
  • PS Seminar Room (N3.022)

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.

Organizers: Lars Mescheder Aseem Behl


From Camera Synchronization to Deep Learning

Talk
  • 06 June 2017 • 14:00 15:00
  • Nadine Rüegg
  • PS greenhouse

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 segmentation task, we instead frame this problem as constructing polygons representing objects. An approach based on Faster R-CNN, a successful object detection algorithm, is presented.

Organizers: Siyu Tang


Human Motion Models

Talk
  • 31 May 2017 • 15:00 16:00
  • Partha Ghosh
  • Aquarium

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 Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time. Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no groundtruth data exists. The proposed protocols can be used to assess generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion- capture datasets available to date and show that our model outperforms the state-of-the-art on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods.

Organizers: Gerard Pons-Moll