Robotics Glossary
A
Action Space: Set of all possible actions a robot can take. Can be discrete (e.g., move forward, turn left) or continuous (e.g., joint velocities).
Actuator: Device that produces motion (e.g., motors, hydraulic cylinders, pneumatic pistons).
AGV (Automated Guided Vehicle): Mobile robot that follows markers or wires on the floor, or uses vision or lasers for navigation.
AMR (Autonomous Mobile Robot): Robot that navigates using sensors and onboard decision-making, without fixed paths.
Anthropomorphic: Resembling human form or characteristics (e.g., humanoid robots).
B
Baseline: Distance between two stereo camera sensors, affecting depth estimation accuracy.
Behavior Tree: Hierarchical structure for robot decision-making, organizing tasks into sequences, selectors, and parallel nodes.
Bipedal Locomotion: Walking on two legs, requiring advanced balance control (ZMP, COM management).
Bounding Box: Rectangle (2D) or cuboid (3D) enclosing a detected object.
C
Calibration: Process of determining accurate parameters (camera intrinsics, joint offsets, sensor biases).
Center of Mass (COM): Point where an object's mass is concentrated, critical for balance control.
Collision Detection: Identifying when robot links intersect with obstacles or self-collide.
Configuration Space (C-Space): Space of all possible joint configurations of a robot.
D
Degrees of Freedom (DOF): Number of independent ways a robot can move. Humanoid arm typically has 7 DOF.
Depth Image: Image where each pixel represents distance to camera (e.g., from RealSense, stereo cameras).
Denavit-Hartenberg (DH): Standard convention for describing robot kinematics using 4 parameters per joint.
Differential Drive: Two-wheel drive system where speed difference creates turning motion.
Digital Twin: Virtual replica of physical robot/environment, synchronized in real-time.
Domain Randomization: Varying simulation parameters (lighting, textures) to improve sim-to-real transfer.
E
Edge Computing: Processing data on local devices (Jetson Orin) rather than cloud, reducing latency.
Embodied AI: AI systems with physical presence that interact with the real world.
End-Effector: Terminal device of a robot arm (gripper, tool, camera).
Exteroceptive Sensor: Sensor that measures external environment (camera, LiDAR, ultrasonic).
F
Force Closure: Grasp configuration that can resist any external force/torque.
Forward Kinematics (FK): Computing end-effector pose from joint angles.
FPS (Frames Per Second): Rate at which images are processed (e.g., 30 FPS for real-time vision).
Friction Cone: Cone of valid contact forces determined by friction coefficient.
G
Gait: Pattern of leg movement during locomotion (e.g., walking, running, trotting).
Gazebo: Open-source robot simulator supporting ROS integration.
GPU Acceleration: Using graphics processors (NVIDIA RTX, Jetson) for parallel computation (perception, physics).
Grasp Planning: Determining where and how to grasp an object for stable manipulation.
H
Holonomic: Robot that can move in any direction instantly (e.g., omnidirectional wheels). Most robots are non-holonomic.
Humanoid: Robot with human-like body structure (head, torso, two arms, two legs).
I
IMU (Inertial Measurement Unit): Sensor measuring acceleration and angular velocity (gyroscope + accelerometer).
Impedance Control: Control strategy creating virtual mass-spring-damper for compliant motion.
Inverse Kinematics (IK): Computing joint angles to achieve desired end-effector pose.
Isaac ROS: NVIDIA's GPU-accelerated ROS 2 packages for perception and navigation.
Isaac Sim: NVIDIA's photorealistic robot simulator built on Omniverse.
J
Jacobian: Matrix relating joint velocities to end-effector velocities, used in IK and control.
Jetson: NVIDIA's edge AI computing platform (Nano, Xavier, Orin) for robotics.
K
Kinematic Chain: Series of rigid links connected by joints.
Kinematics: Study of motion without considering forces.
L
LiDAR (Light Detection and Ranging): Sensor that measures distances using laser pulses, creating 3D point clouds.
LLM (Large Language Model): AI model trained on text (GPT-4, Llama, Claude) used for robot task planning.
Localization: Determining robot's position in an environment (AMCL, particle filter, VSLAM).
M
Manipulation: Moving and interacting with objects using robot arms/grippers.
MATLAB: Programming environment commonly used for robotics algorithms and simulation.
MDP (Markov Decision Process): Mathematical framework for decision-making where outcomes depend only on current state.
MoveIt: Motion planning framework for ROS/ROS 2, using OMPL planners.
Multimodal: Combining multiple data types (vision + language, RGB + depth).
N
Nav2: ROS 2 navigation stack for mobile robots (path planning, obstacle avoidance).
NLU (Natural Language Understanding): Converting human language to structured robot commands.
O
Observation Space: Set of all possible sensor readings a robot can receive.
Occupancy Grid: 2D map where each cell indicates free/occupied/unknown space.
Odometry: Estimating position by integrating wheel encoder or visual feature measurements.
OMPL (Open Motion Planning Library): Library of sampling-based motion planning algorithms (RRT, PRM).
Omniverse: NVIDIA's platform for 3D simulation and collaboration, foundation for Isaac Sim.
P
PhysX: NVIDIA's physics engine used in Isaac Sim for realistic dynamics.
PID Controller: Proportional-Integral-Derivative controller for tracking reference signals.
Pointcloud: Set of 3D points representing object/environment surface (from LiDAR, depth cameras).
Policy: Function mapping observations to actions in reinforcement learning.
Proprioceptive Sensor: Sensor measuring robot's internal state (joint encoders, force/torque sensors).
Q
Quaternion: 4-element representation of 3D rotation (x, y, z, w), avoiding gimbal lock.
QP (Quadratic Programming): Optimization method for whole-body control with multiple objectives.
R
RGBD: Color (RGB) + Depth image format from cameras like RealSense.
Reinforcement Learning (RL): Training robots through trial-and-error with reward signals.
Robot Operating System (ROS): Middleware providing communication, tools, and libraries for robotics.
RRT (Rapidly-exploring Random Tree): Sampling-based motion planning algorithm.
S
SLAM (Simultaneous Localization and Mapping): Building map while tracking robot position.
SDF (Simulation Description Format): XML format for describing robot and world models in Gazebo.
Semantic Segmentation: Labeling each pixel in an image with object class.
Sensor Fusion: Combining data from multiple sensors for improved estimates.
Sim-to-Real: Transferring policies learned in simulation to physical robots.
Stereo Vision: Using two cameras to estimate depth through triangulation.
T
TCP (Tool Center Point): Reference point on robot end-effector for control.
TensorRT: NVIDIA's inference optimizer for deep learning models (INT8, FP16 quantization).
TF (Transform): ROS coordinate frame transformation system (TF2 in ROS 2).
Trajectory: Time-parameterized path defining position, velocity, acceleration.
URDF (Unified Robot Description Format): XML format for describing robot structure and properties.
V
VLA (Vision-Language-Action): System integrating visual perception, language understanding, and robot control.
Visual Servoing: Using visual feedback to control robot motion (IBVS, PBVS).
VSLAM (Visual SLAM): SLAM using cameras instead of LiDAR (ORB-SLAM, Isaac Visual SLAM).
W
Whole-Body Control: Coordinating all robot joints to achieve multiple objectives simultaneously.
Workspace: Set of all positions reachable by robot end-effector.
Z
Zero Moment Point (ZMP): Point on ground where net moment from gravity and inertia is zero, used for bipedal balance.
Zero-Shot: Model generalizing to new tasks/objects without specific training examples.