Sensing and Control for Robust Grasping with Simple Hardware

Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.Engineering and Applied Science

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Detecting and Segmenting Un-occluded Items by Actively Casting Shadows

Abstract. We present a simple and practical approach for segmenting un-occluded items in a scene by actively casting shadows. By ’items’, we refer to objects (or part of objects) enclosed by depth edges. Our approach utilizes the fact that under varying illumination, un-occluded items will cast shadows on occluded items or background, but will not be shadowed themselves. We employ an active illumination approach by taking multiple images under different illumination directions, with illumination source close to the camera. Our approach ignores the texture edges in the scene and uses only the shadow and silhouette information to determine the occlusions. We show that such a segmentation does not require the estimation of a depth map or 3D information, which can be cumbersome, expensive and often fails due to the lack of texture and presence of specular objects in the scene. Our approach can handle complex scenes with self-shadows and specularities. Results on several real scenes along with the analysis of failure cases are presented.

A hierarchical active binocular robot vision architecture for scene exploration and object appearance learning

This thesis presents an investigation of a computational model of hierarchical visual behaviours within an active binocular robot vision architecture. The robot vision system is able to localise multiple instances of the same object class, while simultaneously maintaining vergence and directing its gaze to attend and recognise objects within cluttered, complex scenes. This is achieved by implementing all image analysis in an egocentric symbolic space without creating explicit pixel-space maps and without the need for calibration or other knowledge of the camera geometry. One of the important aspects of the active binocular vision paradigm requires that visual features in both camera eyes must be bound together in order to drive visual search to saccade, locate and recognise putative objects or salient locations in the robot's field of view. The system structure is based on the “attentional spotlight” metaphor of biological systems and a collection of abstract and reactive visual behaviours arranged in a hierarchical structure. Several studies have shown that the human brain represents and learns objects for recognition by snapshots of 2-dimensional views of the imaged scene that happens to contain the object of interest during active interaction (exploration) of the environment. Likewise, psychophysical findings specify that the primate’s visual cortex represents common everyday objects by a hierarchical structure of their parts or sub-features and, consequently, recognise by simple but imperfect 2D view object part approximations. This thesis incorporates the above observations into an active visual learning behaviour in the hierarchical active binocular robot vision architecture. By actively exploring the object viewing sphere (as higher mammals do), the robot vision system automatically synthesises and creates its own part-based object representation from multiple observations while a human teacher indicates the object and supplies a classification name. Its is proposed to adopt the computational concepts of a visual learning exploration mechanism that controls the accumulation of visual evidence and directs attention towards the spatial salient object parts. The behavioural structure of the binocular robot vision architecture is loosely modelled by a WHAT and WHERE visual streams. The WHERE stream maintains and binds spatial attention on the object part coordinates that egocentrically characterises the location of the object of interest and extracts spatio-temporal properties of feature coordinates and descriptors. The WHAT stream either determines the identity of an object or triggers a learning behaviour that stores view-invariant feature descriptions of the object part. Therefore, the robot vision is capable to perform a collection of different specific visual tasks such as vergence, detection, discrimination, recognition localisation and multiple same-instance identification. This classification of tasks enables the robot vision system to execute and fulfil specified high-level tasks, e.g. autonomous scene exploration and active object appearance learning

Bringing the Physical to the Digital

This dissertation describes an exploration of digital tabletop interaction styles, with the ultimate goal of informing the design of a new model for tabletop interaction. In the context of this thesis the term digital tabletop refers to an emerging class of devices that afford many novel ways of interaction with the digital. Allowing users to directly touch information presented on large, horizontal displays. Being a relatively young field, many developments are in flux; hardware and software change at a fast pace and many interesting alternative approaches are available at the same time. In our research we are especially interested in systems that are capable of sensing multiple contacts (e.g., fingers) and richer information such as the outline of whole hands or other physical objects. New sensor hardware enable new ways to interact with the digital. When embarking into the research for this thesis, the question which interaction styles could be appropriate for this new class of devices was a open question, with many equally promising answers. Many everyday activities rely on our hands ability to skillfully control and manipulate physical objects. We seek to open up different possibilities to exploit our manual dexterity and provide users with richer interaction possibilities. This could be achieved through the use of physical objects as input mediators or through virtual interfaces that behave in a more realistic fashion. In order to gain a better understanding of the underlying design space we choose an approach organized into two phases. First, two different prototypes, each representing a specific interaction style – namely gesture-based interaction and tangible interaction – have been implemented. The flexibility of use afforded by the interface and the level of physicality afforded by the interface elements are introduced as criteria for evaluation. Each approaches’ suitability to support the highly dynamic and often unstructured interactions typical for digital tabletops is analyzed based on these criteria. In a second stage the learnings from these initial explorations are applied to inform the design of a novel model for digital tabletop interaction. This model is based on the combination of rich multi-touch sensing and a three dimensional environment enriched by a gaming physics simulation. The proposed approach enables users to interact with the virtual through richer quantities such as collision and friction. Enabling a variety of fine-grained interactions using multiple fingers, whole hands and physical objects. Our model makes digital tabletop interaction even more “natural”. However, because the interaction – the sensed input and the displayed output – is still bound to the surface, there is a fundamental limitation in manipulating objects using the third dimension. To address this issue, we present a technique that allows users to – conceptually – pick objects off the surface and control their position in 3D. Our goal has been to define a technique that completes our model for on-surface interaction and allows for “as-direct-as possible” interactions. We also present two hardware prototypes capable of sensing the users’ interactions beyond the table’s surface. Finally, we present visual feedback mechanisms to give the users the sense that they are actually lifting the objects off the surface. This thesis contributes on various levels. We present several novel prototypes that we built and evaluated. We use these prototypes to systematically explore the design space of digital tabletop interaction. The flexibility of use afforded by the interaction style is introduced as criterion alongside the user interface elements’ physicality. Each approaches’ suitability to support the highly dynamic and often unstructured interactions typical for digital tabletops are analyzed. We present a new model for tabletop interaction that increases the fidelity of interaction possible in such settings. Finally, we extend this model so to enable as direct as possible interactions with 3D data, interacting from above the table’s surface

Compact Environment Modelling from Unconstrained Camera Platforms

Mobile robotic systems need to perceive their surroundings in order to act independently. In this work a perception framework is developed which interprets the data of a binocular camera in order to transform it into a compact, expressive model of the environment. This model enables a mobile system to move in a targeted way and interact with its surroundings. It is shown how the developed methods also provide a solid basis for technical assistive aids for visually impaired people

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D