Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Motion Planning and Posture Control of Multiple n-link Doubly Nonholonomic Manipulators

The paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results. KEYWORDS: Lyapunov-based control scheme; Doubly nonholonomic manipulators; Ghost parking bays; Minimum distance technique; Stability; Kinodynamic constraints

ФЕДЕРАТИВНОЕ ОБУЧЕНИЕ ДЛЯ ВИЗУАЛЬНОГО ОБХОДА ПРЕПЯТСТВИЙ В МОБИЛЬНЫХ РОБОТАХ

Federated learning (FL) is a machine learning approach that allows multiple devices or systems to train a model collaboratively, without exchanging their data. This is particularly useful for autonomous mobile robots, as it allows them to train models customized to their specific environment and tasks, while keeping the data they collect private. Research Objective to train a model to recognize and classify different types of objects, or to navigate around obstacles in its environment. Materials and me¬thods we used FL to train models for a variety of tasks, such as object recognition, obstacle avoidance, localization, and path planning by an autonomous mobile robot operating in a warehouse FL. We equipped the robot with sensors and a processor to collect data and perform machine learning tasks. The robot must communicate with a central server or cloud platform that coordinates the training process and collects model updates from different devices. We trained a neural network (CNN) and used a PID algorithm to generate a control signal that adjusts the position or other variable of the system based on the difference between the desired and actual values, using the relative, integrative and derivative terms to achieve the desired performance. Results through careful design and execution, there are several challenges to implementing FL in autonomous mobile robots, including the need to ensure data privacy and security, and the need to manage communications and the computational resources needed to train the model. Conclusion. We conclude that FL enables autonomous mobile robots to continuously improve their performance and adapt to changing environments and potentially improve the performance of vision-based obstacle avoidance strategies and enable them to learn and adapt more quickly and effectively, leading to more robust and autonomous systems.Федеративное обучение – это подход к машинному обучению, который позволяет нескольким устройствам или системам совместно обучать модель без обмена данными. Это особенно полезно для автономных мобильных роботов, поскольку позволяет им обучать модели, адаптированные к их конкретной среде и задачам, сохраняя конфиденциальность собираемых ими данных. Цель исследования состоит в том, чтобы научить модель распознавать и классифицировать различные типы объектов или обходить препятствия в окружающей среде. Материалы и методы: использовано федеративное машинное обучение для обучения моделей различным задачам, таким как распознавание объектов, обход препятствий, локализация и планирование пути с помощью автономного мобильного робота, работающего на складе. Робот оснащен датчиками и процессором для сбора данных и выполнения задач машинного обучения. Робот должен связываться с центральным сервером или облачной платформой, которая координирует процесс обучения и собирает обновления моделей с разных устройств. Нейронная сеть обучена с использованием алгоритма PID для генерации управляющего сигнала, который регулирует положение или другую переменную системы на основе разницы между желаемыми и фактическими значениями, используя относительные, интегративные и производные условия для достижения желаемой производительности. Результаты. Даже при условии тщательного проектирования и исполнения существует несколько проблем при реализации федеративного обучения в автономных мобильных роботах, включая необходимость обеспечения конфиденциальности и безопасности данных, а также необходимость управления коммуникациями и вычислительными ресурсами, необходимыми для обучения модели. Заключение. Был сделан вывод о том, что федеративное обучение позволяет автономным мобильным роботам постоянно повышать свою производительность и адаптироваться к изменяющимся условиям, а также потенциально улучшать эффективность стратегий обхода препятствий на основе зрения и позволяет им быстрее и эффективнее учиться и адаптироваться, что приводит к созданию более надежных и автономных систем

La Programación Evolutiva Aplicada para Emular Robots

La síntesis de los sistemas de recuperación de información ha aprovechado los puntos de acceso, y las tendencias actuales sugieren que pronto surgirá la simulación de las sumas de verificación. Después de años de investigación técnica en hojas de cálculo se logró demostrar la construcción de agentes para encarnan los principios sólidos de la esteganografía. En este trabajo se investiga cómo aplicar la programación evolutiva en la emulación de robots

Developing concepts for improved efficiency of robot work preparation

SInBot[1] is a large research project that focuses on maximizing the efficient use of mobile industrial robots during medium sized production runs. The system that will be described in this paper will focusses on the development and validation of concepts for efficient work preparation for cells of intelligent mobile robots that execute medium sized production runs. For a wide range of products, the machining tasks will be defined on an appropriate level, enabling control over the robots behavior and performance. When the system, system operator, and robots have more experience with a product the system can be controlled on a higher level (i.e. the subsystems or even robots can start allocating and executing tasks by themselves). Different test beds are used to test the diversity of aspects involved in the development of the SInBot system. The initial test bed used for this research is a combination of two Lynxmotion AL5D robots and a Samsung SUR40 multi-touch environment. In this paper, novel work preparation concepts will be described and an experiment setup is proposed to validate the model for definition and generation of tasks from a CAD file