In recent years, robotics and artificial intelligence (AI) have witnessed significant growth, particularly in self-learning systems. This paper examines the remarkable progress made in this area, with a particular focus on the utilisation of imitation learning. Self-learning robotics systems have demonstrated the autonomous acquisition of new skills, making them highly adaptable and versatile. Imitation learning is a crucial technique that allows robots to gain knowledge from human demonstrations. This paradigm allows machines to learn and replicate human actions, thus enhancing the capabilities of self-learning robotic technology. The primary objective of this research was to investigate the potential of imitation learning and evaluate its impact on the advancement of self-learning robotics. This paper provides a comprehensive overview of self-learning robotic systems using imitation learning, examining the foundational concepts, essential methodologies, and various applications in this intriguing area. Furthermore, we highlight recent developments, discuss current trends, and outline potential research initiatives to guide the continued development of self-learning robotic systems using imitation learning. This review aims to contribute to the evolving landscape of autonomous robotics by consolidating knowledge, identifying challenges, and fostering further innovation in the pursuit of intelligent self-learning machines.

Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Of course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. Further work is still undertaken, and this will focus on learning from more than one expert, optimisation, impact of dynamic manufacturing environment.

Smart Factory is a key platform for recent industrial revolution 4.0 and industrial robotic platform solutions using Artificial Intelligence are an integral measure of its cell’s configuration and reconfiguration. There are two different methods of machine learning used in industrial collaborative robotics systems, Computer Vision Machine Learning and Imitation Learning. Computer vision is a classical use of machine and deep learning methods and it needs a complex, expensive resources and is not suitable for various types of manufacturing automation environment. Imitation Learning is the most fascinating method, and the recent evolving industry is interested on it. The main aim of this research programme is to develop a self-learning robotic system platform solution using Machine and Deep Imitation Learning for smart factories’ industrial applications. A self-learning robotic system using deep imitation learning can reduce working time and give a less human error when performing high-precision processes. It can also improve the ability to configure robotic platform to facilitate a more flexible decisions and cost- effective manufacturing.

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency.