Robot learning of everyday object manipulations via human demonstration

We deal with the problem of teaching a robot to manipulate everyday objects through human demonstration. We first design a task descriptor which encapsulates important elements of a task. The design originates from observations that manipulations involved in many everyday object tasks can be considered as a series of sequential rotations and translations, which we call manipulation primitives. We then propose a method that enables a robot to decompose a demonstrated task into sequential manipulation primitives and construct a task descriptor. We also show how to transfer a task descriptor learned from one object to similar objects. In the end, we argue that this framework is highly generic. Particularly, it can be used to construct a robot task database that serves as a manipulation knowledge base for a robot to succeed in manipulating everyday objects

Computer Vision in the Surgical Operating Room

Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic

Effect of driving experience on anticipatory look-ahead fixations in real curve driving

Peer reviewe

Scene understanding by robotic interactive perception

This thesis presents a novel and generic visual architecture for scene understanding by robotic interactive perception. This proposed visual architecture is fully integrated into autonomous systems performing object perception and manipulation tasks. The proposed visual architecture uses interaction with the scene, in order to improve scene understanding substantially over non-interactive models. Specifically, this thesis presents two experimental validations of an autonomous system interacting with the scene: Firstly, an autonomous gaze control model is investigated, where the vision sensor directs its gaze to satisfy a scene exploration task. Secondly, autonomous interactive perception is investigated, where objects in the scene are repositioned by robotic manipulation. The proposed visual architecture for scene understanding involving perception and manipulation tasks has four components: 1) A reliable vision system, 2) Camera-hand eye calibration to integrate the vision system into an autonomous robot’s kinematic frame chain, 3) A visual model performing perception tasks and providing required knowledge for interaction with scene, and finally, 4) A manipulation model which, using knowledge received from the perception model, chooses an appropriate action (from a set of simple actions) to satisfy a manipulation task. This thesis presents contributions for each of the aforementioned components. Firstly, a portable active binocular robot vision architecture that integrates a number of visual behaviours are presented. This active vision architecture has the ability to verge, localise, recognise and simultaneously identify multiple target object instances. The portability and functional accuracy of the proposed vision architecture is demonstrated by carrying out both qualitative and comparative analyses using different robot hardware configurations, feature extraction techniques and scene perspectives. Secondly, a camera and hand-eye calibration methodology for integrating an active binocular robot head within a dual-arm robot are described. For this purpose, the forward kinematic model of the active robot head is derived and the methodology for calibrating and integrating the robot head is described in detail. A rigid calibration methodology has been implemented to provide a closed-form hand-to-eye calibration chain and this has been extended with a mechanism to allow the camera external parameters to be updated dynamically for optimal 3D reconstruction to meet the requirements for robotic tasks such as grasping and manipulating rigid and deformable objects. It is shown from experimental results that the robot head achieves an overall accuracy of fewer than 0.3 millimetres while recovering the 3D structure of a scene. In addition, a comparative study between current RGB-D cameras and our active stereo head within two dual-arm robotic test-beds is reported that demonstrates the accuracy and portability of our proposed methodology. Thirdly, this thesis proposes a visual perception model for the task of category-wise objects sorting, based on Gaussian Process (GP) classification that is capable of recognising objects categories from point cloud data. In this approach, Fast Point Feature Histogram (FPFH) features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide a probability estimate of the identity of the object and serves the key role of modelling perception confidence in the interactive perception cycle. The interaction stage is responsible for invoking the appropriate action skills as required to confirm the identity of an observed object with high confidence as a result of executing multiple perception-action cycles. The recognition accuracy of the proposed perception model has been validated based on simulation input data using both Support Vector Machine (SVM) and GP based multi-class classifiers. Results obtained during this investigation demonstrate that by using a GP-based classifier, it is possible to obtain true positive classification rates of up to 80\%. Experimental validation of the above semi-autonomous object sorting system shows that the proposed GP based interactive sorting approach outperforms random sorting by up to 30\% when applied to scenes comprising configurations of household objects. Finally, a fully autonomous visual architecture is presented that has been developed to accommodate manipulation skills for an autonomous system to interact with the scene by object manipulation. This proposed visual architecture is mainly made of two stages: 1) A perception stage, that is a modified version of the aforementioned visual interaction model, 2) An interaction stage, that performs a set of ad-hoc actions relying on the information received from the perception stage. More specifically, the interaction stage simply reasons over the information (class label and associated probabilistic confidence score) received from perception stage to choose one of the following two actions: 1) An object class has been identified with high confidence, so remove from the scene and place it in the designated basket/bin for that particular class. 2) An object class has been identified with less probabilistic confidence, since from observation and inspired from the human behaviour of inspecting doubtful objects, an action is chosen to further investigate that object in order to confirm the object’s identity by capturing more images from different views in isolation. The perception stage then processes these views, hence multiple perception-action/interaction cycles take place. From an application perspective, the task of autonomous category based objects sorting is performed and the experimental design for the task is described in detail

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation