Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Robots have begun to populate the everyday environments of human beings. These social robots must perform their tasks without disturbing the people with whom they share their environment. This paper proposes a navigation algorithm for robots that is acceptable to people. Robots will detect the personal areas of humans, to carry out their tasks, generating navigation routes that have less impact on human activities. The main novelty of this work is that the robot will perceive the moods of people to adjust the size of proxemic areas. This work will contribute to making the presence of robots in human-populated environments more acceptable. As a result, we have integrated this approach into a cognitive architecture designed to perform tasks in human-populated environments. The paper provides quantitative experimental results in two scenarios: controlled, including social navigation metrics in comparison with a traditional navigation method, and non-controlled, in robotic competitions where different studies of social robotics are measured.

The use of socially assistive robots in real environments, such as nursing homes or geriatric residences, is spreading in recent years. Social robot navigation in these complex environments, with multiple users and dynamic objects, is an essential task for the next generation of service robots. This navigation must respect social rules, for example not to interrupt an interaction between two people or between a person and an object. Current navigation frameworks in robotics literature do not take into account the social complexity of the scenarios like, for example, the relation of the objects and their use by people with time. This article presents an approach to the idea of time-dependent social mapping. The main novelty is the definition and development of time-dependent affordance spaces. Each object has a zone that vary in function of time and the activities scheduled by the center’s staff. Therefore, the planning of the best path by the robot takes into consideration this variation on time achieving a higher degree of social navigation. Several use cases have been performed in a simulated scenario to asses the robustness and validity of the proposal using these temporal variables.

Robot navigation in human-populated environments is a subject of great interest among the international scientific community. In order to be accepted in these scenarios, it is important for robots to navigate respecting social rules. Avoid getting too close to a person, not interrupting conversations or asking for permission or collaboration when it is required by social conventions, are some of the behaviours that robots must exhibit. This paper presents a social navigation system that integrates different software agents within a cognitive architecture for robots and describes, as the main contribution, the corpus that allows to establish dialogues between robots and humans in real situations to improve the human-aware navigation system. The corpus has been experimentally evaluated by the simulation of different daily situations, where robots need to plan interactions with real people. The results are analysed qualitatively, according to the behaviour expected by the robot in the interaction performed. The results show how the corpus presented in this paper improves the robot navigation, making it more socially accepted.

As autonomous robots become integrated into society, they must socially navigate around humans. We propose that effective social robot navigation relies on three key principles: social norms, perceived safety, and legibility. Our framework, Overlapping Social Navigation Principles, suggests that the strength of each principle is influenced by the presence of other principles. To test our framework, we implemented SRN behaviors on an autonomous robot in a passing scenario and conducted an online study where participants ranked videos of different SRN behavior combinations. Our findings show that incorporating all three principles enhances SRN, with social norms having the greatest impact.

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This article presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.

Robot navigation in large-scale social maps: An action recognition approach

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

To describe performance of a mobile robot requires non-trivial efforts since robot navigation itself comprises various kinds of sub-components and the performance thereof is highly influenced by the surrounding environment and a given task. In this paper, we focus on performance metrics for robot navigation which are intended for indoor service robots. More particularly, we propose a set of performance metrics that may be useful to describe various attributes of robot navigation intended for indoor educational robots. Localization, mapping, and path planning function are considered.

AI-based visual speech recognition towards realistic avatars and lip-reading applications in the metaverse

PurposeThe purpose of this paper is to improve the traditional bug algorithms for the navigation of mobile robots in unknown environments by considering the limitations in previous works such as generating long path, limited to static environments as well as ignoring implementation issues. With this purpose, a new bug‐type algorithm termed Distance Histogram Bug (DH‐Bug) is proposed for overcoming these limitations.Design/methodology/approachDH‐Bug redefines the traditional motion modes and switching criteria to shorten the path length. In order to extend the framework of the traditional bug algorithms to tackle the navigation problem in dynamic environments, a new mode and the related switching conditions are designed for dealing with moving obstacles. Moreover, a realization method termed the Distance Histogram (DH) method which takes many implementation issues into full account is proposed for implementing DH‐Bug on real robots.FindingsDH‐Bug is convergent in static environments and it also guarantees “approximate” convergence based on several reasonable assumptions when there are moving obstacles. Simulations results show that DH‐Bug generates shorter average path length than some classical Bug algorithms in static environments and it also performs well in most simulations that contain moving obstacles except for some extremely adverse scenarios which have been discussed in the paper. Experiments on real robots further verify its applicability in both static environments and dynamic environments containing moving obstacles.Originality/valueCompared with previous works, DH‐Bug has three main contributions. First, in static environments, it can shorten the average path length than many classical bug algorithms in the premise of guaranteeing convergence. Second, it can be applied in dynamic environments containing moving obstacles. Third, unlike the previous bug algorithms that always ignore the practical implementation issues, DH‐Bug presents not only an abstract concept, but also a realization approach for realizing this concept on real robots.

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals.

The future of AI-powered robotics promises a major transformation across various industries, offering remarkable potential to automate complex processes, boost human capabilities, and redefine numerous sectors. By utilizing machine learning, computer vision, and natural language processing, AI-driven robots can interact with their surroundings, make informed decisions, and learn from their experiences, all without direct human oversight. This fusion of robotics and AI enables machines to tackle delicate tasks such as performing surgeries in healthcare, as well as advancing manufacturing and logistics operations. In the near future, AI-powered robots are expected to become increasingly autonomous, adaptable, and proficient in managing tasks within ever-changing environments. Breakthroughs in machine perception will allow robots to better comprehend and respond to the world around them, enhancing safety and effectiveness. Furthermore, advancements in human-robot collaboration may lead to robots working alongside humans in sectors like education, hospitality, and services, improving productivity and user interactions. However, the rise of AI-powered robotics also raises important ethical, legal, and social concerns, such as job loss and privacy issues. To ensure these technologies benefit society, careful integration will be essential. Ultimately, AI-powered robots are set to play a crucial role in shaping the future, transforming how we live and work. Research significance: The future of AI-driven robotics has the potential to revolutionize various industries, including healthcare, manufacturing, logistics, and agriculture. Research in this area concentrates on improving robot autonomy, decision-making, and collaboration with humans through advanced AI technologies. As robots become smarter, they will be able to carry out intricate tasks with greater accuracy, flexibility, and efficiency, boosting productivity and safety. This research also tackles issues such as ethical concerns, reliability, and human-robot emotional interaction. Progress in AI robotics promises innovative, practical, and sustainable solutions, transforming industries and enhancing overall quality of life. Methodology: The approach to studying the future of AI-driven robotics focuses on exploring how artificial intelligence integrates with robotic systems. This involves examining progress in machine learning, computer vision, and natural language processing, which allow robots to complete intricate tasks independently. Researchers investigate technological innovations such as enhanced sensors, edge computing, and human-robot interactions. They also address ethical, societal, and economic implications, such as the effects of automation, labor markets, and safety. To forecast trends, challenges, and opportunities in AI robotics, experts use case studies, simulations, and interviews, offering a well-rounded view of its future potential Alternative: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Evaluation preference: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Results: Hardware advancements are rising to the top, while regulation and ethics are being pushed to the bottom

Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care

Deep learning has been widely used in fields such as natural language processing, computer vision, and driverless vehicles, leading a new wave of artificial intelligence. Deep learning advances however have also been used to create technologies that pose potential threats to national security, social stability, and personal privacy. For example, deepfakes that have recently attracted widespread attention worldwide, which could generate seemingly realistic fake images, audio and video content. This article introduces the background of deepfakes and the principles of deepfakes creation, and then outlines and analyzes the detection methods and datasets for different types of deepfakes, including images, videos, audios, etc. Finally, the article discusses potential research directions and challenges of deepfakes detection and prevention.