Glossary of Terms
This glossary provides definitions for key terms and concepts used throughout the book "Humanoid Robotics & Embodied Intelligence".
A
C
Control Commands: Instructions sent to a robot to dictate its actions and movements. These can vary from low-level joint commands to high-level action requests.
Course Module: A defined unit of learning within the book, structuring content for incremental progression and specific learning outcomes.
D
Digital Twin: A virtual representation of a physical object or system, used for simulation, testing, and analysis in a digital environment.
Dynamics: The study of forces and torques and their effect on the motion of objects, crucial for understanding how robots move and interact with their environment.
E
Embodied Intelligence: The concept that an intelligent agent (like a robot) requires a physical body and real-world interaction to develop and exhibit true intelligence.
Environment Map: A representation of a robot's surroundings, typically built using sensor data, which allows the robot to localize itself and navigate.
G
Gazebo: An open-source 3D robotics simulator widely used for testing robotics algorithms, designing robots, and performing regression testing.
H
Humanoid Robot Model: A digital or physical representation of a robot designed to resemble and/or mimic human form and movement.
I
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, using a combination of accelerometers, gyroscopes, and magnetometers.
Isaac Sim: NVIDIA's scalable robotics simulation application and synthetic data generation tool, built on the Omniverse platform, for developing and testing AI-enabled robots.
K
Kinematics: The study of motion without considering the forces that cause that motion, describing the geometry of motion for robot linkages and joints.
L
LiDAR (Light Detection and Ranging): A remote sensing method that uses pulsed laser to measure ranges (variable distances) to the Earth. In robotics, it's used for mapping and obstacle detection.
N
Natural Language Commands: Text-based instructions given to a robot system, often processed by Vision-Language-Action (VLA) systems, to perform complex tasks.
O
Occupancy Grid: A type of environment map used in robotics where the environment is discretized into a grid, and each cell stores the probability of being occupied by an obstacle.
R
ROS 2 (Robot Operating System 2): An open-source, meta-operating system for robots, providing services like hardware abstraction, low-level device control, inter-process message-passing, and package management.
S
SDF (Simulation Description Format): An XML format for describing objects and environments for robot simulators like Gazebo.
Sensor Data: Information collected by a robot's sensors (e.g., cameras, LiDAR, IMUs) about its internal state and external environment.
SLAM (Simultaneous Localization and Mapping): A computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
U
Unity: A cross-platform game engine that is also used as a powerful platform for robotics simulation, especially for visually rich and interactive environments.
URDF (Unified Robot Description Format): An XML file format used in ROS to describe all elements of a robot, including its visual appearance, collision properties, and inertial properties.
V
Vision-Language-Action (VLA) Systems: AI systems that integrate visual perception, natural language understanding, and physical action capabilities to enable robots to respond to human commands in complex environments.