Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step required for training perceptions systems used in autonomous driving. The proposed generative framework, coined Generative One-Shot Learning (GOL), takes as input single one-shot objects, or generic patterns, and a small set of so-called regularization samples used to drive the generative process. New synthetic data is generated as Pareto optimal solutions from one-shot objects using a set of generalization functions built into a generalization generator. GOL has been evaluated on environment perception challenges encountered in autonomous vision.Comment: Web-site: http://rovislab.com/gol.htm

User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home

In this article, we describe the development of a human-robot interaction concept for service robots to assist elderly people in the home with physical tasks. Our approach is based on the insight that robots are not yet able to handle all tasks autonomously with sufficient reliability in the complex and heterogeneous environments of private homes. We therefore employ remote human operators to assist on tasks a robot cannot handle completely autonomously. Our development methodology was user-centric and iterative, with six user studies carried out at various stages involving a total of 241 participants. The concept is under implementation on the Care-O-bot 3 robotic platform. The main contributions of this article are (1) the results of a survey in form of a ranking of the demands of elderly people and informal caregivers for a range of 25 robot services, (2) the results of an ethnography investigating the suitability of emergency teleassistance and telemedical centers for incorporating robotic teleassistance, and (3) a user-validated human-robot interaction concept with three user roles and corresponding three user interfaces designed as a solution to the problem of engineering reliable service robots for home environments

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

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