Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches

Deep Reinforcement Learning Approaches for Technology Enhanced Learning

Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives. In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS. However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS. This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems. To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training. Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively. Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI-Robotics systems has enhanced the quality our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that make up AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide users, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field