Editing AI for Robotics (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Robot''' — A physical machine capable of sensing its environment and executing physical actions; may be autonomous, semi-autonomous, or teleoperated.
* '''Actuator''' — A motor or mechanism that produces physical movement (joint motors, hydraulic cylinders, pneumatic actuators).
* '''Sensor''' — A device that measures aspects of the environment (cameras, LIDAR, force/torque sensors, proprioception).
* '''Proprioception''' — The robot's sense of its own joint positions, velocities, and forces; analogous to human body awareness.
* '''Forward kinematics''' — Computing the position and orientation of the robot's end-effector given its joint angles.
* '''Inverse kinematics (IK)''' — Computing the joint angles needed to reach a desired end-effector position and orientation.
* '''End-effector''' — The robot's "hand" — the tool or gripper at the end of the arm that interacts with the environment.
* '''Grasp''' — The act of picking up or holding an object with the gripper; grasp planning is a major research area.
* '''Manipulation''' — The ability to handle, move, and interact with objects in the environment.
* '''Locomotion''' — The ability to move the robot's base through the environment (walking, driving, flying).
* '''Sim-to-real gap''' — The difference between the simulated training environment and the real physical world; a major challenge for RL-based robotics.
* '''Domain randomization''' — A sim-to-real transfer technique that trains with randomized simulation parameters (friction, mass, lighting) so the policy generalizes to the real world.
* '''Imitation learning''' — Learning robot behavior from human demonstrations rather than RL reward signals.
* '''Teleoperation''' — A human controlling a robot remotely, often used to collect demonstration data.
* '''Foundation model for robotics''' — Large pre-trained models (RT-2, π0, OpenVLA) that generalize across robot tasks using multimodal inputs.
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