Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, 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 and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

Interactive Perception based on Gaussian Process Classification Applied to Household Object Recognition & Sorting

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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

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Sensing Highly Non-Rigid Objects with RGBD Sensors for Robotic Systems

The goal of this research is to enable a robotic system to manipulate clothing and other highly non-rigid objects using an RGBD sensor. The focus of this thesis is to define and test various algorithms / models that are used to solve parts of the laundry process (i.e. handling, classifying, sorting, unfolding, and folding). First, a system is presented for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item is removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a dataset of known items. The contributions of this part of the laundry process are a novel method for extracting articles of clothing from a pile of laundry, a novel method of classifying clothing using interactive perception, and a multi-layer approach termed L-M-H, more specifically L-C-S-H for clothing classification. This thesis describes two different approaches to classify clothing into categories. The first approach relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. Experiments from the first approach demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction. The second approach relies upon color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The multi-layer approach compartmentalizes the problem into a high (H) layer, multiple mid-level (characteristics(C), selection masks(S)) layers, and a low (L) layer. This approach produces \u27local\u27 solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories, +17.90% for four categories, and +10.35% for seven categories over the baseline system, using support vector machines. Second, an algorithm is presented for automatically unfolding a piece of clothing. A piece of cloth is pulled in different directions at various points of the cloth in order to flatten the cloth. The features of the cloth are extracted and calculated to determine a valid location and orientation in which to interact with it. The features include the peak region, corner locations, and continuity / discontinuity of the cloth. In this thesis, a two-stage algorithm is presented, introducing a novel solution to the unfolding / flattening problem using interactive perception. Simulations using 3D simulation software, and experiments with robot hardware demonstrate the ability of the algorithm to flatten pieces of laundry using different starting configurations. These results show that, at most, the algorithm flattens out a piece of cloth from 11.1% to 95.6% of the canonical configuration. Third, an energy minimization algorithm is presented that is designed to estimate the configuration of a deformable object. This approach utilizes an RGBD image to calculate feature correspondence (using SURF features), depth values, and boundary locations. Input from a Kinect sensor is used to segment the deformable surface from the background using an alpha-beta swap algorithm. Using this segmentation, the system creates an initial mesh model without prior information of the surface geometry, and it reinitializes the configuration of the mesh model after a loss of input data. This approach is able to handle in-plane rotation, out-of-plane rotation, and varying changes in translation and scale. Results display the proposed algorithm over a dataset consisting of seven shirts, two pairs of shorts, two posters, and a pair of pants. The current approach is compared using a simulated shirt model in order to calculate the mean square error of the distance from the vertices on the mesh model to the ground truth, provided by the simulation model

Visual Perception of Garments for their Robotic Manipulation

Tématem předložené práce je strojové vnímání textilií založené na obrazové informaci a využité pro jejich robotickou manipulaci. Práce studuje několik reprezentativních textilií v běžných kognitivně-manipulačních úlohách, jako je například třídění neznámých oděvů podle typu nebo jejich skládání. Některé z těchto činností by v budoucnu mohly být vykonávány domácími robotickými pomocníky. Strojová manipulace s textiliemi je poptávaná také v průmyslu. Hlavní výzvou řešeného problému je měkkost a s tím související vysoká deformovatelnost textilií, které se tak mohou nacházet v bezpočtu vizuálně velmi odlišných stavů.The presented work addresses the visual perception of garments applied for their robotic manipulation. Various types of garments are considered in the typical perception and manipulation tasks, including their classification, folding or unfolding. Our work is motivated by the possibility of having humanoid household robots performing these tasks for us in the future, as well as by the industrial applications. The main challenge is the high deformability of garments, which can be posed in infinitely many configurations with a significantly varying appearance

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields