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Glossary of Physical AI Terms

This glossary provides definitions for key terms used throughout the Physical AI book.

  • Action: In ROS 2, a long-running, goal-oriented communication mechanism with feedback.
  • Actuator: A component of a machine that is responsible for moving and controlling a mechanism or system.
  • AI-Robot Brain: Refers to the intelligent software and algorithms enabling a robot's cognitive functions, often integrating perception, planning, and learning.
  • Digital Twin: A virtual representation of a physical object or system, used for simulation, analysis, and monitoring.
  • Docker: A platform for developing, shipping, and running applications in containers.
  • Gazebo: A powerful 3D robotics simulator.
  • GPU (Graphics Processing Unit): A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images, crucial for AI/ML.
  • Humanoid Robot: A robot designed to resemble the human body, particularly its form and movements.
  • IMU (Inertial Measurement Unit): An electronic device that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body.
  • Isaac ROS: A collection of hardware-accelerated packages for ROS 2, leveraging NVIDIA GPUs.
  • Isaac Sim: NVIDIA's scalable robotics simulation application and development environment built on Omniverse.
  • LiDAR: A remote sensing method that uses pulsed laser to measure distances to objects.
  • LLM (Large Language Model): A type of AI model capable of understanding and generating human-like text, often used in robotics for high-level task planning.
  • Node: In ROS 2, a process that performs computation, forming the basic unit of execution.
  • Perception Pipeline: A series of processing steps to extract meaningful information from sensor data for a robot.
  • Physical AI: A branch of AI focused on intelligent systems that interact with the physical world, often embodied in robots.
  • rclpy: The Python client library for ROS 2.
  • Reinforcement Learning (RL): An area of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
  • ROS 2 (Robot Operating System 2): A flexible framework for writing robot software.
  • Robot Operating System (ROS): A set of software libraries and tools that help you build robot applications.
  • Sensor: A device that detects and responds to some type of input from the physical environment.
  • SDF (Simulation Description Format): An XML format for describing objects and environments for robot simulators like Gazebo.
  • Synthetic Data: Data generated artificially, often used for training AI models when real-world data is scarce.
  • Topic: In ROS 2, a named bus over which nodes exchange messages, enabling asynchronous communication.
  • Unity: A real-time 3D development platform often used for high-fidelity Human-Robot Interaction (HRI) visuals.
  • URDF (Unified Robot Description Format): An XML format for describing robots in a way that is easily parsed by software.
  • VLA (Vision-Language-Action): A pipeline that integrates visual perception, natural language understanding (often via LLMs), and robotic action execution.
  • Whisper: An OpenAI model for robust speech recognition, used to convert spoken language into text.