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.
Dimensionality reduction applied to neural ensemble data has led to the concept of a 'neural trajectory', a low-dimensional representation of how the state of the network evolves over time. Here we present a novel neural trajectory extraction algorithm which combines spike train distance metrics (Victor and Purpura, 1996) with dimensionality reduction based on local neighborhood statistics (van der Maaten and Hinton, 2008.) . We apply this technique to describe and quantify the activity of primate ventral premotor cortex neuronal ensembles in the context of a cued reaching and grasping task with instructed delay.
Humans interact with their environment in a highly flexible manner. One important component for the successful control of such flexible interactions is an internal body model. To maintain a consistent internal body model, the brain appears to continuously and probabilistically integrate multiple sources of information, including various sensory modalities but also anticipatory, re-afferent information about current body motion. A modular, multimodal arm model (MMM) is presented.
The model represents a seven degree of freedom arm in various interactive modality frames. The modality frames distinguish between proprioceptive, limb-relative orientation, head-relative orientation, and head-relative location frames. Each arm limb is represented separately but highly interactively in each of these modality frames. Incoming sensory and motor feedback information is continuously exchanged in a rigorous, probabilistic fashion, while a consistent overall arm model is maintained due to the local interactions.
The model is able to automatically identify sensory failures and sensory noise. Moreover, it is able to mimic the rubber hand illusion phenomenon. Currently, we endow the model with neural representations for each modality frame to play-out its full potential for planning and goal-directed control.
The amount of digital video content available is growing daily, on sites such as YouTube. Recent statistics on the YouTube website show that around 48 hours of video are uploaded every minute. This massive data production calls for automatic analysis.
In this talk we present some recent results for action recognition in videos. Bag-of-features have shown very good performance for action recognition in videos. We briefly review the underlying principles and introduce trajectory-based video features, which have shown to outperform the state of the art. These trajectory features are obtained by dense point sampling and tracking based on displacement information from a dense optical flow field. Trajectory descriptors are obtained with motion boundary histograms, which are robust to camera motion. We, then, show how to integrate temporal structure into a bag-of-features based on an actom sequence model. Action sequence models localize actions based on sequences of atomic actions, i.e., represent the temporal structure by sequences of histograms of actom-anchored visual features. This representation is flexible, sparse and discriminative. The resulting actom sequence model is shown to significantly improve performance over existing methods for temporal action localization.
Finally, we show how to move towards more structured representations by explicitly modeling human-object interactions. We learn how to represent human actions as interactions between persons and objects. We localize in space and track over time both the object and the person, and represent an action as the trajectory of the object with respect to the person position, i.e., our human-object interaction features capture the relative trajectory of the object with respect to the human. This is joint work with A Gaidon, V. Ferrari, Z. Harchaoui, A. Klaeser, A. Prest, H. Wang.
The supervision of public spaces aims at multiple objectives, such as early acquisition of targets, their identification and pursuit throughout the supervised area. To achieve these, typical sensors such as pan-tilt-zoom cameras need to either focus on individuals, or provide a broad field of view, which are conflicting control settings. We address this problem in an information-theoretic manner: by phrasing each of the objectives in terms of mutual information, they become comparable. The problem turns into maximisation of information, which is predicted for the next time step and phrased as a decision process.
Our approach results in decisions that on average satisfy objectives in desired proportions. At the end of the talk I will address an application of information maximisation to aid in the interactive calibration of cameras.
Recovering the depth of a scene is important for bridging the gap between the real and the virtual world, but also for tasks such as segmenting objects in cluttered scenes. Very cheap single view depth imaging cameras, i.e. Time of Fight cameras (ToF) or Microsoft's Kinect system, are entering the mass consumer market. In general, the acquired images have a low spatial resolution and suffer from noise as well as technology specific artifacts. In this talk I will present algorithmic solutions to the entire depth imaging pipeline, ranging from preprocessing to depth image analysis. For enhancing image intensity and depth maps, a higher order total variation based approach has been developed which exhibits superior results as compared to current state-of-the-art approaches. This performance has been achieved by allowing jumps across object boundaries, computed both from the image gradients and the depth maps. Within objects, staircasing effects as observed in standard total variation approaches is circumvented by higher order regularization. The 2.5 D motion or range flow of the observed scenes is computed by a combined global-local approach.
Particularly on Kinect-data, best results were achieved by discarding information on object edges. These are prone to errors due to the data acquisition process. In conjunction with a calibration procedure, this leads to very accurate and robust motion estimation. On these computed range flow data, we have developed the estimation of robust, scale- and rotation-invariant features. These make it feasible to use our algorithms for a novel approach to gesture recognition for man-machine interactions. This step is currently work inprogress and I will present very promising first results.
For evaluating the results of our algorithms, we plan to use realistic simulations and renderings. We have made significant advances in analyzing the feasibility of these synthetic test images and data. The bidirectional reflectance distribution function (BRDF) of several objects have been measured using a purpose-build “light-dome” setup. This, together with the development of an accurate stereo-acquisition system for measuring 3D-objects lays the ground work for performing realistic renderings. Additionally, we have started to create a test-image database with ground truth for depth, segmentation and light-field data.
3D scanning of moving objects has many applications, for example, marker-less motion capture, analysis on fluid dynamics, object explosion and so on. One of the approach to acquire accurate shape is a projector-camera system, especially the methods that reconstructs a shape by using a single image with static pattern is suitable for capturing fast moving object. In this research, we propose a method that uses a grid pattern consisting of sets of parallel lines. The pattern is spatially encoded by a periodic color pattern. While informations are sparse in the camera image, the proposed method extracts the dense (pixel-wise) phase informations from the sparse pattern.
As the result, continuous regions in the camera images can be extracted by analyzing the phase. Since there remain one DOF for each region, we propose the linear solution to eliminate the DOF by using geometric informations of the devices, i.e. epipolar constraint. In addition, solution space is finite because projected pattern consists of parallel lines with same intervals, the linear equation can be efficiently solved by integer least square method.
In the experiments, a scanning system that can capture an object in fast motion has been actually developed by using a high-speed camera. In the experiments, we show the sequence of dense shapes of an exploding balloon, and other objects at more than 1000 fps.
Fitting statistical 2D and 3D shape models to images is necessary for a variety of tasks, such as video editing and face recognition. Much progress has been made on local fitting from an initial guess, but determining a close enough initial guess is still an open problem. One approach is to detect distinct landmarks in the image and initialize the model fit from these correspondences. This is difficult, because detection of landmarks based only on their local appearance is inherently ambiguous, making it necessary to use global shape information for the detections. We propose a method to solve the combinatorial problem of selecting out of a large number of candidate landmark detections the configuration which is best supported by a shape model.
Our method, as opposed to previous approaches, always finds the globally optimal configuration. The algorithm can be applied to a very general class of shape models and is independent of the underlying feature point detector.
This talk concerns the use of physics-based models for human pose tracking and scene inference. We outline our motivation for physics-based models, some results with monocular pose tracking in terms of biomechanically inspired controllers, and recent results on the inference of scene interactions. We show that physics-based models facilitate the estimation of physically plausible human motion with little or no mocap data required. Scene interactions play an integral role in modeling sources of external forces acting on the body.
In spite of the significant effort that has been devoted to the core problems of object and action recognition in images and videos, the recognition performance of state of the art algorithms is well below what would be required for any successful deployment in many applications. Additionally, there are challenging combinatorial problems associated with constructing globally “optimal” descriptions of images and videos in terms of potentially very large collections of object and action models. The constraints that are utilized in these optimization procedures are loosely referred to as “context.” So, for example, vehicles are generally supported by the ground, so that an estimate of ground plane location parameters in an image constrains positions and apparent sizes of vehicles. Another source of context are the everyday spatial and temporal relationships between objects and actions; so, for example, keyboards are typically “on” tables and not “on” cats.
The first part of the talk will discuss how visually grounded models of object appearance and relations between objects can be simultaneously learned from weakly labeled images (images which are linguistically but not spatially annotated – i.e., we are told there is a car in the image, but not where the car is located).
Next, I will discuss how these models can be more efficiently learned using active learning methods. Once these models are acquired, one approach to inferring what objects appear in a new image is to segment the image into pieces, construct a graph based on the regions in the segmentation and the relationships modeled, and then apply probabilistic inference to the graph. However, this typically results in a very dense graph with many “noisy” edges, leading to inefficient and inaccurate inference. I will briefly describe a learning approach that can construct smaller and more informative graphs for inference.
Finally, I will relax the (unreasonable) assumption that one can segment an image into regions that correspond to objects, and describe an approach that can simultaneously construct instances of objects out of collections of connected segments that look like objects, while also softly enforcing contextual constraints.
Organizers: Michel Besserve
Human pose estimation from monocular images is one of the most challenging and computationally demanding problems in computer vision. Standard models such as Pictorial Structures consider interactions between kinematically-connected joints or limbs, leading to inference quadratic in the number of pixels.
As a result, researchers and practitioners have restricted themselves to simple models which only measure the quality of limb-pair possibilities by their 2D geometric plausibility. In this talk, we propose novel methods which allow for efficient inference in richer models with data-dependent interaction cliques.
First, we introduce structured prediction cascades, a structured analog of binary cascaded classifiers, which learn to focus computational effort where it is needed, filtering out many states cheaply while ensuring the correct output is unfiltered.
Second, we propose a way to decompose models of human pose with cyclic dependencies into a collection of tree models, and provide novel methods to impose model agreement. These techniques allow for sparse and efficient inference on the order of minutes per image or video clip.
As a result, we can afford to model pairwise interaction potentials much more richly with data-dependent features such as contour continuity, segmentation alignment, color consistency, optical flow and more.
Finally, we apply these techniques to higher-order cliques, extending the idea of poselets to structured models. We show empirically that these richer models are worthwhile, obtaining significantly more accurate pose estimation on popular datasets.
Organizers: Michel Besserve