Robotics

These terms highlight key areas of overlap between AI/ML and robotics, illustrating how concepts from AI and ML are applied to give robots the ability to perceive, reason, and act in the physical world.

Action Recognition - In robotics, this involves the ability of robots to understand and interpret human actions or the actions of other robots, which can be crucial for collaborative tasks and human-robot interaction.

Agent - In the context of robotics, an agent can refer to a robot itself, which perceives its environment through sensors and acts upon it through actuators, based on the decision-making processes embedded in its control systems.

Artificial Intelligence (AI) - AI is foundational to modern robotics, enabling robots to perform tasks autonomously, make decisions, and adapt to their environments through learning and optimization.

Deep Learning (DL) - This subset of machine learning is used in robotics for tasks such as vision recognition, decision-making, and language understanding, allowing robots to process complex inputs and learn from them.

Markov Decision Process (MDP) - A mathematical framework for modeling decision-making, which is used in robotics for planning and control, especially in uncertain or dynamic environments.

Reinforcement Learning (RL) - A learning paradigm used in robotics for developing control policies that allow robots to learn optimal actions through trial and error, interacting with their environment to achieve specific goals.