A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.

Automated Planning: Theory and Practice.

Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowledge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step toward addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Nonmonotonic logical inference in ASP is used to generate a multinomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains.

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Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective

This document discloses one or more systems, apparatuses, methods, etc. for detecting precise indoor location of a portable wireless device based on a WiFi simultaneous localization and mapping (SLAM) algorithm that implements spatial and temporal coherence. In an implementation, a SLAM algorithm includes WiFi similarities and inertial navigational system (INS) measurements data as location estimates (i.e., references) for the spatial and temporal coherences implementations to constitute the WiFi SLAM algorithm.

Simultaneous localization and mapping using spatial and temporal coherence for indoor location

Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gesture. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor multi-room environments. The human can see through obstacles to observe the robots’ current states and intentions, and provide feedback, while the robots’ behaviors are then adjusted toward human-multi-robot teamwork. Experiments have been conducted with real robots and human participants using collaborative delivery tasks. Results show that ARROCH outperformed a standard non-AR approach in both user experience and teamwork efficiency. In addition, we have developed a novel simulation environment using Unity (for AR and human simulation) and Gazebo (for robot simulation). Results in simulation demonstrate ARROCH’s superiority over AR-based baselines in human-robot collaboration.

ARROCH: Augmented Reality for Robots Collaborating with a Human

In the past few years, numerous Deep Neural Network (DNN) models and frameworks have been developed to tackle the problem of real-time object detection from RGB images. Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment. On the other hand, mobile Augmented Reality (AR) frameworks can continuously track a camera’s pose within the scene and can estimate the correct scale of the environment by using Visual-Inertial Odometry (VIO). In this paper, we propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices. Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map. Each component takes advantage of the different characteristics of the VIO-based AR framework. We implemented the AR-enhanced features using ARCore and the SSD Mobilenet model on Android phones. To validate our approach, we manually labeled objects in image sequences taken from 12 room-scale AR sessions. The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.

Object Detection in the Context of Mobile Augmented Reality

Although mobile robots are increasingly being used in real-world applications, the ability to robustly sense and interact with the environment is still missing. A key requirement for the widespread deployment of mobile robots is the ability to operate autonomously by learning desired environmental models and revising the learned models in response to environmental changes. This paper presents an approach that enables a mobile robot to autonomously learn layered models for environmental objects using temporal, local and global visual cues. A temporal assessment of image gradient features is used to detect candidate objects, which are then modeled using color distribution statistics and a spatial representation of gradient features. The robot incrementally revises the learned models and uses them for object recognition and tracking based on a matching scheme comprising a spatial similarity measure and second order distribution statistics. All algorithms are implemented and tested on a wheeled robot platform in dynamic indoor environments.

Autonomous learning of vision-based layered object models on mobile robots

As robots are increasingly deployed in complex real-world domains, visual object recognition continues to be an open problem. Existing algorithms for learning and recognizing objects are predominantly computationally expensive, and require considerable training or domain knowledge. Our algorithm enables robots to use motion cues to identify and focus on a set of interesting objects, automatically extracting appearance-based and contextual cues from a small number of images to efficiently learn representative models of these objects. Learned models exploit complementary strengths of: (a) relative spatial arrangement of gradient features; (b) graph-based models of neighborhoods of gradient features; (c) parts-based models of image segments; (d) color distributions; and (e) mixture models of local context. The learned models are used in conjunction with an energy minimization algorithm and a generative model of information fusion for reliable and efficient recognition in novel scenes. The algorithm is evaluated on mobile robots in indoor and outdoor domains, and on images from benchmark datasets.

Move and the robot will learn: Vision-based autonomous learning of object models

Mobile robots are increasingly being used in real-world applications due to the ready availability of high-fidelity sensors and the development of sophisticated information processing algorithms. However, one key challenge to the widespread deployment of mobile robots equipped with multiple sensors and processing algorithms is the ability to autonomously tailor sensing and information processing to the task at hand. This paper poses this challenge as the task of planning under uncertainty, and more specifically as an instance of probabilistic sequential decision-making. A novel hierarchy of partially observable Markov decision processes (POMDPs) is incorporated, which uses constrained-convolutional policies and automatic belief propagation to achieve efficient and reliable operation on mobile robots. All algorithms are implemented and evaluated on simulated and physical robot platforms for the task of searching for target objects in dynamic indoor environments.

Xiang Li

Papers

ARROCH: Augmented Reality for Robots Collaborating with a Human

Object Detection in the Context of Mobile Augmented Reality

Autonomous learning of vision-based layered object models on mobile robots

Move and the robot will learn: Vision-based autonomous learning of object models

To look or not to look: A hierarchical representation for visual planning on mobile robots