REVIEW OF ROBOTIC TECHNOLOGY FOR STRAWBERRY PRODUCTION

With an increasing world population in need of food and a limited amount of land for cultivation, higher efficiency in agricultural production, especially fruits and vegetables, is increasingly required. The success of agricultural production in the marketplace depends on its quality and cost. The cost of labor for crop production, harvesting, and post-harvesting operations is a major portion of the overall production cost, especially for specialty crops such as strawberry. As a result, a multitude of automation technologies involving semi-autonomous and autonomous robots have been utilized, with an aim of minimizing labor costs and operation time to achieve a considerable improvement in farming efficiency and economic performance. Research and technologies for weed control, harvesting, hauling, sorting, grading, and/or packing have been generally reviewed for fruits and vegetables, yet no review has been conducted thus far specifically for robotic technology being used in strawberry production. In this article, studies on strawberry robotics and their associated automation technologies are reviewed in terms of mechanical subsystems (e.g., traveling unit, handling unit, storage unit) and electronic subsystems (e.g., sensors, computer, communication, and control). Additionally, robotic technologies being used in different stages in strawberry production operations are reviewed. The robot designs for strawberry management are also categorized in terms of purpose and environment

Large Language Models for Robotics: A Survey

The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning. We first provide an overview of the background and development of LLMs for robotics, followed by a description of the benefits of LLMs for robotics and recent advancements in robotics models based on LLMs. We then delve into the various techniques used in the model, including those employed in perception, decision-making, control, and interaction. Finally, we explore the applications of LLMs in robotics and some potential challenges they may face in the near future. Embodied intelligence is the future of intelligent science, and LLMs-based robotics is one of the promising but challenging paths to achieve this.Comment: Preprint. 4 figures, 3 table

Pushing the limits of Visual Grounding: Pre-training on large synthetic datasets

openVisual Grounding is a crucial computer vision task requiring a deep understanding of data semantics. Leveraging the transformative trend of training controllable generative models, the research aims to demonstrate the substantial improvement of state-of-the-art visual grounding models through the use of massive, synthetically generated data. The study crafts a synthetic dataset using controllable generative models, offering a scalable solution to overcome challenges in traditional data collection processes. The study introduces a synthetic dataset, employing controllable generative models for scalability. Evaluating visual grounding model (TransVG) — on the synthetic dataset showcases promising results, with attributes contributing to a diverse dataset of 250,000 samples. The resulting datasets showcases the impact of synthetic data on visual grounding evolution, contributing to advancements in this dynamic field.Visual Grounding is a crucial computer vision task requiring a deep understanding of data semantics. Leveraging the transformative trend of training controllable generative models, the research aims to demonstrate the substantial improvement of state-of-the-art visual grounding models through the use of massive, synthetically generated data. The study crafts a synthetic dataset using controllable generative models, offering a scalable solution to overcome challenges in traditional data collection processes. The study introduces a synthetic dataset, employing controllable generative models for scalability. Evaluating visual grounding model (TransVG) — on the synthetic dataset showcases promising results, with attributes contributing to a diverse dataset of 250,000 samples. The resulting datasets showcases the impact of synthetic data on visual grounding evolution, contributing to advancements in this dynamic field

The Evolution of Evolvability in Evolutionary Robotics

Previous research has demonstrated that computational models of Gene Regulatory Networks (GRNs) can adapt so as to increase their evolvability, where evolvability is defined as a population’s responsiveness to environmental change. In such previous work, phenotypes have been represented as bit strings formed by concatenating the activations of the GRN after simulation. This research is an extension where previous results supporting the evolvability of GRNs are replicated, however, the phenotype space is enriched with time and space dynamics with an evolutionary robotics task environment. It was found that a GRN encoding used in the evolution of a way-point navigation behavior in a fluctuating environment results in (robot controller) populations becoming significantly more responsive (evolvable) over time. This is as compared to a direct encoding of controllers which was unable to improve it’s evolvability in the same task environment

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Autonomous On-Line Learning of Assistant Selection Policies For Fault Recovery

As robotic space exploration has expanded, increasing the autonomy of planetary exploration is necessary to overcome limitations of human-centric robotic operations. Although autonomous systems are designed to be robust, an exploration environment offers unknowns by its very definition; planning for failure handling must be an important part of mission design. This work considers the problem of how robots in space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified fixed rules, which are not responsive to dynamic environments or uncertainty in human performance. First, a novel and highly flexible reinforcement-learning based assistant selection framework is described that uses contextual multi-arm bandit algorithms. The contextual bandits exploit information from observations about their environment and potential assistants to efficiently learn selection rules, called policies, under a wide set of uncertain operating conditions and unknown/dynamically constrained assistant capabilities. The framework is first assessed for usefulness within simulation by comparing with other state of the art assistant selection approaches. Having discovered that the contextual bandit approach outperforms conventional static policies and non-contextual learning approaches with favorable robustness and scaling properties, a human factors study is designed and executed to gather real-world human performance data. On real humans, contextual bandit achieves better performance compared to baseline approaches.While useful, the dataset was not designed to capture effects of long-term learning in humans. In simulation, dynamics associated with human learning are used to assess the robustness of several contextual multi-arm bandit algorithms to situations occuring with real humans.Finally, two follow on experiments are described to bring adaptive assistant selection closer to a real-life mission deployment. As a result of this work, autonomous systems can learn to request the best assistance available.</p

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms