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
Today’s advances in tactile sensing and wearable, IOT and context-aware computing are spurring new ideas about how to configure touch-centered interactions in terms of roles and utility, which in turn expose new technical and social design questions. But while haptic actuation, sensing and control are improving, incorporating them into a real-world design process is challenging and poses a major obstacle to adoption into everyday technology. Some classes of haptic devices, e.g., grounded force feedback, remain expensive and limited in range. I’ll describe some recent highlights of an ongoing effort to understand how to support haptic designers and end-users. These include a wealth of online experimental design tools, and DIY open sourced hardware and accessible means of creating, for example, expressive physical robot motions and evolve physically sensed expressive tactile languages. Elsewhere, we are establishing the value of haptic force feedback in embodied learning environments, to help kids understand physics and math concepts. This has inspired the invention of a low-cost, handheld and large motion force feedback device that can be used in online environments or collaborative scenarios, and could be suitable for K-12 school contexts; this is ongoing research with innovative education and technological elements. All our work is available online, where possible as web tools, and we plan to push our research into a broader openhaptics effort.
Organizers: Katherine Kuchenbecker
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
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.
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.
Markov random fields (MRFs) have found widespread use as models of natural image and scene statistics. Despite progress in modeling image properties beyond gradient statistics with high-order cliques, and learning image models from example data, existing MRFs only exhibit a limited ability of actually capturing natural image statistics.
In this talk I will present recent work that investigates this limitation of previous filter-based MRF models, including Fields of Experts (FoEs). We found that these limitations are due to inadequacies in the leaning procedure and suggest various modifications to address them. These "secrets of FoE learning" allow training more suitable potential functions, whose shape approaches that of a Dirac-delta function, as well as models with larger and more filters.
Our experiments not only indicate a substantial improvement of the models' ability to capture relevant statistical properties of natural images, but also demonstrate a significant performance increase in a denoising application to levels previously unattained by generative approaches. This is joint work with Qi Gao.
The great majority of object analysis methods are based on visual object properties - objects are categorized according to how they appear in images. Visual appearance is measured in terms of image features (e.g., SIFTs) extracted from images or video. However, besides appearance, objects also have many properties that can be of interest, e.g., for a robot who wants to employ them in activities: Temperature, weight, surface softness, and also the functionalities or affordances of the object, i.e., how it is intended to be used. One example, recently addressed in the vision community, are chairs. Chairs can look vastly different, but have one thing in common: they afford sitting. At the Computer Vision and Active Perception Lab at KTH, we study the problem of inferring non-observable object properties in a number of ways. In this presentation I will describe some of this work.
Shape analysis and modeling of 2D and 3D objects has important applications in many branches of science and engineering. The general goals in shape analysis include: derivation of efficient shape metrics, computation of shape templates, representation of dominant shape variability in a shape class, and development of probability models that characterize shape variation within and across classes. While past work on shape analysis is dominated by point representations -- finite sets of ordered or triangulated points on objects' boundaries -- the emphasis has lately shifted to continuous formulations.
The shape analysis of parametrized curves and surfaces introduces an additional shape invariance, the re-parametrization group, in additional to the standard invariants of rigid motions and global scales. Treating re-parametrization as a tool for registration of points across objects, we incorporate this group in shape analysis, in the same way orientation is handled in Procrustes analysis. For shape analysis of parametrized curves, I will describe an elastic Riemannian metric and a mathematical representation, called square-root-velocity-function (SRVF), that allows optimal registration and analysis using simple tools.
This framework provides proper metrics, geodesics, and sample statistics of shapes. These sample statistics are further useful in statistical modeling of shapes in different shape classes. Then, I will describe some preliminary extensions of these ideas to shape analysis of parametrized surfaces, I will demonstrate these ideas using applications from medical image analysis, protein structure analysis, 3D face recognition, and human activity recognition in videos.
Abstract: We can modify the optical properties of surfaces by “coating” them with a micron-thin membrane supported by an elastomeric gel. Using an opaque, matte membrane, we can make reflected light micrographs with a distinctive SEM-like appearance. These have modest magnification (e.g., 50X), but they reveal fine surface details not normally seen with an optical microscope.
The system, which we call “GelSight,” removes optical complexities such as specular reflection, albedo, and subsurface scattering, and isolates the shading information that signals 3D shape. One can then see the topography of optically challenging subjects like sandpaper, machined metal, and living human skin. In addition, one can capture 3D surface geometry through photometric stereo. This leads to a non-destructive contact-based optical profilometer that is simple, fast, and compact.
Human can easily see 3D shape from single 2D images, exploiting multiple kinds of information. This has given rise to multiple subfields (in both human vision and computer vision) devoted to the study of shape-from-shading, shape-from-texture, shape-from-contours, and so on.
The proposed algorithms for each type of shape-from-x remain specialized and fragile (in contrast with the flexibility and robustness of human vision). Recent work in graphics and psychophysics has demonstrated the importance of local orientation structure in conveying 3D shape. This information is fairly stable and reliable, even when a given shape is rendered in multiple styles (including non-photorealistic styles such as line drawings.)
We have developed an exemplar-based system (which we call Shape Collage) that learns to associate image patches with corresponding 3D shape patches. We train it with synthetic images of “blobby” objects rendered in various ways, including solid texture, Phong shading, and line drawings. Given a new image, it finds the best candidate scene patches and assembles them into a coherent interpretation of the object shape.
Our system is the first that can retrieve the shape of naturalistic objects from line drawings. The same system, without modification, works for shape-from-texture and can also get shape from shading, even with non-Lambertian surfaces. Thus disparate types of image information can be processed by a single mechanism to extract 3D shape. Collaborative work with Forrester Cole, Phillip Isola, Fredo Durand, and William Freeman.
A central aspect of visual processing in the retina is the existence of nonlinear subunits within the receptive fields of retinal ganglion cells. These subunits have been implicated in visual computations such as segregation of object motion from background motion. However, relatively little is known about the spatial structure of subunits and its emergence from nonlinear interactions in the interneuron circuitry of the retina.
We used physiological measurements of functional circuitry in the isolated primate retina at single-cell resolution, combined with novel computational approaches, to explore the neural computations that produce subunits. Preliminary results suggest that these computations can be understood in terms of convergence of photoreceptor signals via specific types of interneurons to ganglion cells.
Considerable research has demonstrated that the representation is not equally faithful throughout the visual field; representation appears to be coarser in peripheral vision, perhaps as a strategy for dealing with an information bottleneck in visual processing. In the last few years, a convergence of evidence has suggested that in peripheral and unattended regions, the information available consists of summary statistics.
For a complex set of statistics, such a representation can provide a rich and detailed percept of many aspects of a visual scene. However, such a representation is also lossy; we would expect the inherent ambiguities and confusions to have profound implications for vision.
For example, a complex pattern, viewed peripherally, might be poorly represented by its summary statistics, leading to the degraded recognition experienced under conditions of visual crowding. Difficult visual search might occur when summary statistics could not adequately discriminate between a target-present and distractor-only patch of the stimuli. Certain illusory percepts might arise from valid interpretations of the available – lossy – information. It is precisely visual tasks upon which a statistical representation has significant impact that provide the evidence for such a representation in early vision. I will summarize recent evidence that early vision computes summary statistics based upon such tasks.
Human body movements are highly complex spatio-temporal patterns and their control and recognition represent challenging problems for technical as well as neural systems. The talk will present an overview of recent work of our group, exploiting biologically-inspired learning-based reprensentations for the recognition and synthesis of body motion.
The first part of the talk will present a neural theory for the visual processing of goal-directed actions, which reproduces and partially correctly predicts electrophysiological results from action-selective cortical neurons in monkey cortex. In particular, we show that the same neural circuits might account for the recognition of natural and abstract action stimuli.
In the second part of the talk different techniques for the learning of structured online-capable synthesis models for complex body movements are discussed. One approach is based on the learning of kinematic primitives, exploiting anechoic demixing, and the generation of such primitives by networks of canonical dynamical systems.
An approach for the design of a stable overall system dynamics of such nonlinear networks is discussed. The second approach is the learning of hierarchical models for interactive movements, combining Gaussian Process Latent Variable models and Gaussian process Dynamical Models, and resulting in animations that pass the Turing test of computer graphics. The presented work was funded by the DFG, and EC FP 7 projects SEARISE, TANGO and AMARSI.
Variations in lighting can have a significant effect on the appearance of an object. Modeling these variations is important for object recognition and shape reconstruction, particularly of smooth, textureless objects. The recent decade has seen significant progress in handling lambertian objects. In that context I will present our work on using harmonic representations to represent the reflectance of lambertian objects under complex lighting configurations and their application to photometric stereo and prior-assisted shape from shading. In addition, I will present preliminary results in handling specular objects and methods for dealing with moving objects.