A field that focuses on swarm robotic systems that can adjust their behaviors based on real-time data and environmental changes.

A subset of artificial intelligence that enables systems to learn from data and improve their performance without explicit programming.

Collective behavior of decentralized systems, often seen in nature, that can be harnessed for robotic applications.

An optimization algorithm inspired by social behavior of birds, used to find optimal solutions in complex spaces.

A type of machine learning where agents learn to make decisions by receiving rewards or penalties based on their actions.

The capability to process data as it is generated, allowing immediate responses to changes in the environment.

A system structure where outputs are circled back as inputs, enhancing system adaptability and learning.

A virtual setup where swarm robotic behaviors can be tested and evaluated under controlled conditions.

Systems capable of performing tasks without human intervention, often relying on AI and machine learning.

Changing conditions in which robotic systems operate, requiring adaptability and real-time decision-making.

An in-depth analysis of a real-world application or instance, providing insights into successful strategies and outcomes.

A machine learning approach where models are trained on labeled data, allowing for prediction and classification.

The process of creating an early model of a system to test and refine concepts before full-scale development.

Actions or strategies employed by robots that change in response to environmental feedback.

The process of inspecting, cleansing, and modeling data to discover useful information for decision-making.

Groups composed of members from different fields, collaborating to tackle complex problems in robotics.

A document summarizing the findings from testing and analysis, assessing performance and areas for improvement.

A step-by-step procedure or formula for solving a problem, fundamental to programming and robotics.

The ability of a robotic system to react quickly and appropriately to changes in its environment.

A collection of data used to train machine learning models, crucial for effective learning and performance.

Modifications made to enhance the functionality and performance of robotic systems based on feedback and testing.

Working jointly with others to achieve common goals, essential in advanced robotics projects.

A robotic system that operates as intended, demonstrating the desired adaptive behaviors in real-time.

Evaluating two or more systems to identify strengths and weaknesses, guiding improvements.

A repetitive approach to design that incorporates feedback and testing to refine and enhance systems.