12,648 research outputs found

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction

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    Employing skin-like tactile sensors on robots enhances both the safety and usability of collaborative robots by adding the capability to detect human contact. Unfortunately, simple binary tactile sensors alone cannot determine the context of the human contact -- whether it is a deliberate interaction or an unintended collision that requires safety manoeuvres. Many published methods classify discrete interactions using more advanced tactile sensors or by analysing joint torques. Instead, we propose to augment the intention recognition capabilities of simple binary tactile sensors by adding a robot-mounted camera for human posture analysis. Different interaction characteristics, including touch location, human pose, and gaze direction, are used to train a supervised machine learning algorithm to classify whether a touch is intentional or not with an F1-score of 86%. We demonstrate that multimodal intention recognition is significantly more accurate than monomodal analyses with the collaborative robot Baxter. Furthermore, our method can also continuously monitor interactions that fluidly change between intentional or unintentional by gauging the user's attention through gaze. If a user stops paying attention mid-task, the proposed intention and attention recognition algorithm can activate safety features to prevent unsafe interactions. We also employ a feature reduction technique that reduces the number of inputs to five to achieve a more generalized low-dimensional classifier. This simplification both reduces the amount of training data required and improves real-world classification accuracy. It also renders the method potentially agnostic to the robot and touch sensor architectures while achieving a high degree of task adaptability.Comment: 11 pages, 8 figures, preprint under revie

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Reinforcement Learning from Passive Data via Latent Intentions

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    Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passive data, despite not having reward or action labels, can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions: measuring how the likelihood of future outcomes change when the agent acts to achieve a particular task. We propose a temporal difference learning objective to learn about intentions, resulting in an algorithm similar to conventional RL, but which learns entirely from passive data. When optimizing this objective, our agent simultaneously learns representations of states, of policies, and of possible outcomes in an environment, all from raw observational data. Both theoretically and empirically, this scheme learns features amenable for value prediction for downstream tasks, and our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.Comment: Accompanying website at https://dibyaghosh.com/icvf

    One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

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    OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

    Continual Learning of Hand Gestures for Human-Robot Interaction

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    In this paper, we present an efficient method to incrementally learn to classify static hand gestures. This method allows users to teach a robot to recognize new symbols in an incremental manner. Contrary to other works which use special sensors or external devices such as color or data gloves, our proposed approach makes use of a single RGB camera to perform static hand gesture recognition from 2D images. Furthermore, our system is able to incrementally learn up to 38 new symbols using only 5 samples for each old class, achieving a final average accuracy of over 90\%. In addition to that, the incremental training time can be reduced to a 10\% of the time required when using all data available

    Grand challenges in entomology: Priorities for action in the coming decades

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    Entomology is key to understanding terrestrial and freshwater ecosystems at a time of unprecedented anthropogenic environmental change and offers substantial untapped potential to benefit humanity in a variety of ways, from improving agricultural practices to managing vector-borne diseases and inspiring technological advances. We identified high priority challenges for entomology using an inclusive, open, and democratic four-stage prioritisation approach, conducted among the membership and affiliates (hereafter ‘members’) of the UK-based Royal Entomological Society (RES). A list of 710 challenges was gathered from 189 RES members. Thematic analysis was used to group suggestions, followed by an online vote to determine initial priorities, which were subsequently ranked during an online workshop involving 37 participants. The outcome was a set of 61 priority challenges within four groupings of related themes: (i) ‘Fundamental Research’ (themes: Taxonomy, ‘Blue Skies’ [defined as research ideas without immediate practical application], Methods and Techniques); (ii) ‘Anthropogenic Impacts and Conservation’ (themes: Anthropogenic Impacts, Conservation Options); (iii) ‘Uses, Ecosystem Services and Disservices’ (themes: Ecosystem Benefits, Technology and Resources [use of insects as a resource, or as inspiration], Pests); (iv) ‘Collaboration, Engagement and Training’ (themes: Knowledge Access, Training and Collaboration, Societal Engagement). Priority challenges encompass research questions, funding objectives, new technologies, and priorities for outreach and engagement. Examples include training taxonomists, establishing a global network of insect monitoring sites, understanding the extent of insect declines, exploring roles of cultivated insects in food supply chains, and connecting professional with amateur entomologists. Responses to different challenges could be led by amateur and professional entomologists, at all career stages. Overall, the challenges provide a diverse array of options to inspire and initiate entomological activities and reveal the potential of entomology to contribute to addressing global challenges related to human health and well-being, and environmental change

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Experiments with active and driven synthetic colloids in complex fluids

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    In this review, we focus on recent experimental research involving active colloidal particles of non-biological origin evolving in non-Newtonian fluids. This includes self-propelling active particles and particles driven by external fields. We present different propulsion strategies that are either enabled, or strongly modified, by the presence of a complex medium. This paves the way for novel mechanisms of active transport in biofluids or in other non-Newnotian fluids. When considering the medium, we differentiate between disordered complex fluids, such as diluted polymer solutions, and liquid crystals. While the latter are also viscoelastic fluids, the ability to control their molecular orientation results in distinct colloidal driving and steering mechanisms, and enables new types of active soft matter in the form of active quasi-particles
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