Lindsey the Tour Guide Robot: Adaptive Long-Term Autonomy in Social Environments

This project proposes a framework for online adaptation of robot behaviours deployed autonomously in social settings with the goal of increasing the overall users' engagement during the interactions. One of the most critical aspects to address for robots deployed in ``the real world'' is the necessity of interacting with people, whether intentionally or not. Interacting with people requires a wide range of capabilities, from perceiving the different people's intentions and emotional states to generating appropriate behaviours for the specific context of the interaction. Moreover, it requires that robots learn and adapt from experience while interacting with their users. In this project, a mobile robot is embedded in a long-term study in a public museum. The robot has been deployed for more than a year, to date, as an autonomous tour guide to the museum's visitors, with its tasks being guiding people to the position of various exhibits and giving a description of each item. The long-term scenario allows studying how people interact with a robot in an unconstrained setting and give the opportunity of improving the current state-of-the-art robotics autonomy in a social setting. The initial data collection shows that users' engagement during the robotised tours steeply declines after the initial moments of the interaction. The first main contribution of this project is to investigate whether it is possible to automatically assess the users' engagement from the robot point-of-view during the interactions. A dataset of robot ego-centric videos was collected and manually annotated by independent coders with continuous engagement values. From it, an end-to-end regression model was trained to predict engagement from the robot point of view from a single camera. Experimental evaluation shows that the model accurately estimates the engagement level of people during an interaction, even in diverse environments and with different robots. Once the robot can detect the engagement state of users during the interactions, it can potentially plan tangential behaviours to influence the users' attentional state itself. The second contribution of this work is devising an online reinforcement learning algorithm that allows the robot to adapt its behaviour online from the feedback obtained during the interactions. The feedback is obtained from users' engagement values estimated from the robot head camera. In the experimental evaluation, the robot delivers the usual tours to the users with the difference that the choice of some actions is left to the adaptive learning algorithm. Results show that after a few months of exploration, the robot successfully learns a policy that leads people to stay in the interaction for longer

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Trust in Robots

Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation