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Institute Talks

Programming Intelligence through Geometry, Topology, and Anisotropy

Talk
  • 28 June 2019 • 11:00 12:00
  • Prof. Shu Yang
  • 2P04

Geometry is concerned with the properties of configurations of points, lines, and circles, while topology is concerned with space, dimension, and transformation. Geometry is also materials independent and scale invariant. By introducing holes and cuts in 2D sheets, we demonstrate dramatic shape change and super-conformability via expanding or collapsing of the hole arrays without deforming individual lattice units. When choosing the cuts and geometry correctly, we show folding into the third dimension, known as kirigami. The kirigami structures can be rendered pluripotent, that is changing into different 3D structures from the same 2D sheet. We explore their potential applications in energy efficient building facade, super-stretchable and shape conformable energy storage devices and medical devices, as well as bioinspired robotics. Programmable shape-shifting materials can take different physical forms to achieve multifunctionality in a dynamic and controllable manner. Through designs of geometric surface patterns, e.g. microchannels, we program the orientational elasticity in liquid crystal elastomers (LCEs), to direct folding of the 2D sheets into 3D shapes, which can be triggered by heat, light, and electric field. Taking this knowledge of guided inhomogeneous local deformations in LCEs, we then tackle the inverse problem – pre-programming geometry on a flat sheet to take an arbitrary desired 3D shape. Lastly, I will show the prospective of taking geometry to create smart fabrics and tendon-like filaments for soft robotic applications.

Thermoacoustic instabilities: various approaches to estimation and control

Talk
  • 01 July 2019 • 14:00 15:00
  • Florent Di Meglio
  • MPI-IS Stuttgart, Heisenbergstr. 3, seminar room 2P4

Reducing the size and emissions of gas turbine engines used in the aeronautics industry forces manufacturers to explore new operating conditions. An undesirable phenomenon called thermo-acoustic instabilities may occur, caused by the coupling between combustion dynamics and the acoustics of the combustion chamber. To help predict, detect and suppress it, we explore various approaches. We will discuss the design of observers for infinite-dimensional systems, Fourier-based reduced-order modeling as well as a Machine-Learning approach based on high-fidelity simulation data.

Organizers: Sebastian Trimpe Mona Buisson-Fenet

Uri Shalit - TBA

IS Colloquium
  • 08 July 2019 • 11:15 a.m. 12:15 a.m.
  • Uri Shalit

Organizers: Krikamol Muandet

Recognizing the Pain Expressions of Horses

Talk
  • 10 December 2018 • 14:00 15:00
  • Prof. Dr. Hedvig Kjellström
  • Aquarium (N3.022)

Recognition of pain in horses and other animals is important, because pain is a manifestation of disease and decreases animal welfare. Pain diagnostics for humans typically includes self-evaluation and location of the pain with the help of standardized forms, and labeling of the pain by an clinical expert using pain scales. However, animals cannot verbalize their pain as humans can, and the use of standardized pain scales is challenged by the fact that animals as horses and cattle, being prey animals, display subtle and less obvious pain behavior - it is simply beneficial for a prey animal to appear healthy, in order lower the interest from predators. We work together with veterinarians to develop methods for automatic video-based recognition of pain in horses. These methods are typically trained with video examples of behavioral traits labeled with pain level and pain characteristics. This automated, user independent system for recognition of pain behavior in horses will be the first of its kind in the world. A successful system might change the concept for how we monitor and care for our animals.


Robot Learning for Advanced Manufacturing – An Overview

Talk
  • 10 December 2018 • 11:00 12:00
  • Dr. Eugen Solowjow
  • MPI-IS Stuttgart, seminar room 2P4

A dominant trend in manufacturing is the move toward small production volumes and high product variability. It is thus anticipated that future manufacturing automation systems will be characterized by a high degree of autonomy, and must be able to learn new behaviors without explicit programming. Robot Learning, and more generic, Autonomous Manufacturing, is an exciting research field at the intersection of Machine Learning and Automation. The combination of "traditional" control techniques with data-driven algorithms holds the promise of allowing robots to learn new behaviors through experience. This talk introduces selected Siemens research projects in the area of Autonomous Manufacturing.

Organizers: Sebastian Trimpe Friedrich Solowjow


  • Prof. Holger Stark
  • Stuttgart 2P4

Active motion of biological and artificial microswimmers is relevant in the real world, in microfluidics, and biological applications but also poses fundamental questions in non-equi- librium statistical physics. Mechanisms of single microswimmers either designed by nature or in the lab need to be understood and a detailed modeling of microorganisms helps to explore their complex cell design and their behavior. It also motivates biomimetic approaches. The emergent collective motion of microswimmers generates appealing dynamic patterns as a consequence of the non-equilibrium.

Organizers: Metin Sitti Zoey Davidson


  • Umar Iqbal
  • PS Aquarium

In this talk, I will present an overview of my Ph.D. research towards articulated human pose estimation from unconstrained images and videos. In the first part of the talk, I will present an approach to jointly model multi-person pose estimation and tracking in a single formulation. The approach represents body joint detections in a video by a spatiotemporal graph and solves an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. I will also introduce the PoseTrack dataset and benchmark which is now the de-facto standard for multi-person pose estimation and tracking. In the second half of the talk, I will present a new method for 3D pose estimation from a monocular image through a novel 2.5D pose representation. The new 2.5D representation can be reliably estimated from an RGB image. Furthermore, it allows to exactly reconstruct the absolute 3D body pose up to a scaling factor, which can be estimated additionally if a prior of the body size is given. I will also describe a novel CNN architecture to implicitly learn the heatmaps and depth-maps for human body key-points from a single RGB image.

Organizers: Dimitrios Tzionas


  • Prof. Dr. Rahmi Oklu
  • 3P02

Minimally invasive approaches to vascular disease and cancer have revolutionized medicine. I will discuss novel approaches to vascular bleeding, aneurysm treatment and tumor ablation.

Organizers: Metin Sitti


  • Prof. Eric Tytell
  • MPI-IS Stuttgart, Werner-Köster lecture hall

Many fishes swim efficiently over long distances to find food or during migrations. They also have to accelerate rapidly to escape predators. These two behaviors require different body mechanics: for efficient swimming, fish should be very flexible, but for rapid acceleration, they should be stiffer. Here, I will discuss recent experiments that show that they can use their muscles to tune their effective body mechanics. Control strategies inspired by the muscle activity in fishes may help design better soft robotic devices.

Organizers: Ardian Jusufi


  • Prof. Dr. Stefan Roth
  • N0.002

Supervised learning with deep convolutional networks is the workhorse of the majority of computer vision research today. While much progress has been made already, exploiting deep architectures with standard components, enormous datasets, and massive computational power, I will argue that it pays to scrutinize some of the components of modern deep networks. I will begin with looking at the common pooling operation and show how we can replace standard pooling layers with a perceptually-motivated alternative, with consistent gains in accuracy. Next, I will show how we can leverage self-similarity, a well known concept from the study of natural images, to derive non-local layers for various vision tasks that boost the discriminative power. Finally, I will present a lightweight approach to obtaining predictive probabilities in deep networks, allowing to judge the reliability of the prediction.

Organizers: Michael Black


A fine-grained perspective onto object interactions

Talk
  • 30 October 2018 • 10:30 11:30
  • Dima Damen
  • N0.002

This talk aims to argue for a fine-grained perspective onto human-object interactions, from video sequences. I will present approaches for the understanding of ‘what’ objects one interacts with during daily activities, ‘when’ should we label the temporal boundaries of interactions, ‘which’ semantic labels one can use to describe such interactions and ‘who’ is better when contrasting people perform the same interaction. I will detail my group’s latest works on sub-topics related to: (1) assessing action ‘completion’ – when an interaction is attempted but not completed [BMVC 2018], (2) determining skill or expertise from video sequences [CVPR 2018] and (3) finding unequivocal semantic representations for object interactions [ongoing work]. I will also introduce EPIC-KITCHENS 2018, the recently released largest dataset of object interactions in people’s homes, recorded using wearable cameras. The dataset includes 11.5M frames fully annotated with objects and actions, based on unique annotations from the participants narrating their own videos, thus reflecting true intention. Three open challenges are now available on object detection, action recognition and action anticipation [http://epic-kitchens.github.io]

Organizers: Mohamed Hassan


Artificial Haptic Intelligence for Human-Machine Systems

IS Colloquium
  • 25 October 2018 • 11:00 12:00
  • Veronica J. Santos
  • N2.025 at MPI-IS in Tübingen

The functionality of artificial manipulators could be enhanced by artificial “haptic intelligence” that enables the identification of object features via touch for semi-autonomous decision-making and/or display to a human operator. This could be especially useful when complementary sensory modalities, such as vision, are unavailable. I will highlight past and present work to enhance the functionality of artificial hands in human-machine systems. I will describe efforts to develop multimodal tactile sensor skins, and to teach robots how to haptically perceive salient geometric features such as edges and fingertip-sized bumps and pits using machine learning techniques. I will describe the use of reinforcement learning to teach robots goal-based policies for a functional contour-following task: the closure of a ziplock bag. Our Contextual Multi-Armed Bandits approach tightly couples robot actions to the tactile and proprioceptive consequences of the actions, and selects future actions based on prior experiences, the current context, and a functional task goal. Finally, I will describe current efforts to develop real-time capabilities for the perception of tactile directionality, and to develop models for haptically locating objects buried in granular media. Real-time haptic perception and decision-making capabilities could be used to advance semi-autonomous robot systems and reduce the cognitive burden on human teleoperators of devices ranging from wheelchair-mounted robots to explosive ordnance disposal robots.

Organizers: Katherine J. Kuchenbecker Adam Spiers


Artificial Haptic Intelligence for Human-Machine Systems

IS Colloquium
  • 24 October 2018 • 11:00 12:00
  • Veronica J. Santos
  • 5H7 at MPI-IS in Stuttgart

The functionality of artificial manipulators could be enhanced by artificial “haptic intelligence” that enables the identification of object features via touch for semi-autonomous decision-making and/or display to a human operator. This could be especially useful when complementary sensory modalities, such as vision, are unavailable. I will highlight past and present work to enhance the functionality of artificial hands in human-machine systems. I will describe efforts to develop multimodal tactile sensor skins, and to teach robots how to haptically perceive salient geometric features such as edges and fingertip-sized bumps and pits using machine learning techniques. I will describe the use of reinforcement learning to teach robots goal-based policies for a functional contour-following task: the closure of a ziplock bag. Our Contextual Multi-Armed Bandits approach tightly couples robot actions to the tactile and proprioceptive consequences of the actions, and selects future actions based on prior experiences, the current context, and a functional task goal. Finally, I will describe current efforts to develop real-time capabilities for the perception of tactile directionality, and to develop models for haptically locating objects buried in granular media. Real-time haptic perception and decision-making capabilities could be used to advance semi-autonomous robot systems and reduce the cognitive burden on human teleoperators of devices ranging from wheelchair-mounted robots to explosive ordnance disposal robots.

Organizers: Katherine J. Kuchenbecker