Imitation learning techniques are methods in artificial intelligence where a computer or robot learns to perform tasks by observing demonstrations, usually from a human expert. Instead of programming every action or rule, the system watches and tries to mimic the behaviour it sees. This approach helps machines learn complex tasks quickly by copying examples, making…
Category: Autonomous Systems
Autonomous Workflow Optimization
Autonomous workflow optimisation refers to the use of intelligent systems or software that can automatically analyse, adjust, and improve the steps involved in a business process without requiring constant human input. These systems monitor how work is being done, identify inefficiencies or bottlenecks, and make changes to streamline tasks. The goal is to save time,…
Intelligent Task Scheduling
Intelligent task scheduling is the use of smart algorithms and automation to decide when and how tasks should be carried out. It aims to organise work in a way that makes the best use of time, resources, and priorities. By analysing factors like deadlines, task dependencies, and available resources, intelligent task scheduling helps ensure that…
Multi-Agent Coordination
Multi-agent coordination is the process where multiple independent agents, such as robots, software programs, or people, work together to achieve a shared goal or complete a task. Each agent may have its own abilities, information, or perspective, so they need to communicate, share resources, and make decisions that consider the actions of others. Good coordination…
Hierarchical Policy Learning
Hierarchical policy learning is a method in machine learning where a complex task is divided into smaller, simpler tasks, each managed by its own policy or set of rules. These smaller policies are organised in a hierarchy, with higher-level policies deciding which lower-level policies to use at any moment. This structure helps break down difficult…
Augmented Reality Workflows
Augmented Reality (AR) workflows are processes that combine digital information or graphics with the real world, allowing users to interact with both at the same time. These workflows often use smartphones, tablets or specialised glasses to overlay virtual guides, instructions or visual data onto physical objects and spaces. By doing this, AR workflows help people…
Spiking Neural Networks
Spiking Neural Networks, or SNNs, are a type of artificial neural network designed to work more like the human brain. They process information using spikes, which are brief electrical pulses, rather than continuous signals. This makes them more energy efficient and suitable for certain tasks. SNNs are particularly good at handling data that changes over…
Neuromorphic Computing
Neuromorphic computing is a type of technology that tries to mimic the way the human brain works by designing computer hardware and software that operates more like networks of neurons. Instead of following traditional computer architecture, neuromorphic systems use structures that process information in parallel and can adapt based on experience. This approach aims to…
Sim-to-Real Transfer
Sim-to-Real Transfer is a technique in robotics and artificial intelligence where systems are trained in computer simulations and then adapted for use in the real world. The goal is to use the speed, safety, and cost-effectiveness of simulations to develop skills or strategies that can work outside the virtual environment. This process requires addressing differences…
Domain Randomisation
Domain randomisation is a technique used in artificial intelligence, especially in robotics and computer vision, to make models more robust. It involves exposing a model to many different simulated environments where aspects like lighting, textures, and object positions are changed randomly. By training on these varied scenarios, the model learns to perform well even when…