What do forest fires, disease outbreaks, robot swarms, and social networks have in common? How can we develop a common set of tools for these applications? In this talk, I will first introduce a modeling framework that describes large-scale phenomena and which is based on the idea of "local interactions." I will then describe my work on creating estimation and control methods for a single agent and for a cooperative team of autonomous agents. In particular, these algorithms are scalable as the solution does not change if the number of agents or environment size changes. Forest fires and the 2013 Ebola outbreak in West Africa are presented as examples.
Organizers: Sebastian Trimpe
Theories of motor control in neuroscience usually focus on the role of the nervous system in the coordination of movement. However, the literature in sports science as well as in embodied robotics suggests that improvements in motor performance can be achieved through an improvement of the body mechanical properties themselves, rather than only the control. I therefore developed the thesis that efficient motor coordination in animals and humans relies on the adjustment of the body mechanical properties to the task at hand, by the postural system.
As part of a proposed design for a surgical robot on the space station, my research group has been asked to look at controls that can provide literally surgical precision. Due to excessive time delay, we envision a system with a local model being controlled by a surgeon while the remote system on the space station follows along in a safe manner. Two of the major design considerations that come into play for the low-level feedback loops on the remote side are 1) the harmonic drives in a robot will cause excessive vibrations in a micro-gravity environment unless active damping strategies are employed and 2) when interacting with a human tissue environment the robot must apply smooth control signals that result in precise positions and forces. Thus, we envision intelligent strategies that utilize nonlinear, adaptive, neural-network, and/or fuzzy control theory as the most suitable. However, space agencies, or their engineering sub-contractors, typically provide gain and phase margin characteristics as requirements to the engineers involved in a control system design, which are normally associated with PID or other traditional linear control schemes. We are currently endeavouring to create intelligent controls that have guaranteed gain and phase margins using the Cerebellar Model Articulation Controller.
Organizers: Katherine Kuchenbecker
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 Kuchenbecker
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.
Two talks for the price of one! I will present my recent work on the challenging problem of stereo matching of scenes with little or no surface texture, attacking the problem from two very different angles. First, I will discuss how surface orientation priors can be added to the popular semi-global matching (SGM) algorithm, which significantly reduces errors on slanted weakly-textured surfaces. The orientation priors serve as a soft constraint during matching and can be derived in a variety of ways, including from low-resolution matching results and from monocular analysis and Manhattan-world assumptions. Second, we will examine the pathological case of Mondrian Stereo -- synthetic scenes consisting solely of solid-colored planar regions, resembling paintings by Piet Mondrian. I will discuss assumptions that allow disambiguating such scenes, present a novel stereo algorithm employing symbolic reasoning about matched edge segments, and discuss how similar ideas could be utilized in robust real-world stereo algorithms for untextured environments.
Organizers: Anurag Ranjan
Non-planar object deformations result in challenging but informative signal variations. We aim to recover this information in a feedforward manner by employing discriminatively trained convolutional networks. We formulate the task as a regression problem and train our networks by leveraging upon manually annotated correspondences between images and 3D surfaces. In this talk, the focus will be on our recent work "DensePose", where we form the "COCO-DensePose" dataset by introducing an efficient annotation pipeline to collect correspondences between 50K persons appearing in the COCO dataset and the SMPL 3D deformable human-body model. We use our dataset to train CNN-based systems that deliver dense correspondences 'in the wild', namely in the presence of background, occlusions, multiple objects and scale variations. We experiment with fully-convolutional networks and region-based DensePose-RCNN model and observe a superiority of the latter; we further improve accuracy through cascading, obtaining a system that delivers highly accurate results in real time (http://densepose.org).
Organizers: Georgios Pavlakos
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.
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.
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.
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.
Gaussian Processes are a principled, practical, probabilistic approach to learning in flexible non-parametric models and have found numerous applications in regression, classification, unsupervised learning and reinforcement learning. Inference, learning and prediction can be done exactly on small data sets with Gaussian likelihood. In more realistic application with large scale data and more complicated likelihoods approximations are necessary. The variational framework for approximate inference in Gaussian processes has emerged recently as a highly effective and practical tool. I will review and demonstrate the capabilities of this framework applied to non-linear state space models.
Organizers: Philipp Hennig
Taking advantages of state-of-art micro/nanotechnologies, fascinating functional biomaterials and integrated biosystems, we can address numerous important problems in fundamental biology as well as clinical applications in cancer diagnosis and treatment.
Organizers: Peer Fischer