Physical AI & Robotics Visuals
How robots sense, move, and learn, from feedback loops and coordinate frames to motion planning and learned policies, taught through numbered SVG sequences
The Sense–Think–Act Loop
How robots observe changing environments, plan actions, and correct for delays and disturbances in closed loops.
See it visuallyRobot Anatomy and Degrees of Freedom
Distinguish joint space from task space, calculate mobility with Grübler's formula, and map physical workspace limits.
See it visuallyHow Robots Sense
Compare proprioceptive wheel encoders with exteroceptive range sensors, calculate slip drift, and discover why multi-sensor fusion is mandatory.
See it visuallyCoordinate Frames and Transforms
Convert coordinates across sensor and robot frames using homogeneous transformation matrices in SE(2) and SE(3).
See it visuallyForward and Inverse Kinematics
Predict end-effector positions with forward kinematics and solve multi-branch joint angles with analytical inverse kinematics.
See it visuallyFeedback Control and PID
Master proportional, integral, and derivative control, understand steady-state gravity droop, and discover why derivative noise causes chatter.
See it visuallyState Estimation and the Kalman Filter
Follow the recursive predict-update cycle of the Kalman filter as it fuses noisy wheel odometry with range sensor measurements to estimate robot pose.
See it visuallyMotion Planning
Navigate around obstacles by transforming robot geometry into configuration space, searching occupancy grids with A*, and timing kinodynamic trajectories.
See it visuallyRobot Learning: Imitation to RL
Train robotic manipulation policies using behavior cloning, fix quadratic covariate drift with DAgger, and bridge sim-to-real transfer with domain randomization.
See it visuallyVision-Language-Action Models
Connect semantic language instructions to physical manipulator motions using multimodal transformer backbones, action tokenization, and hierarchical execution loops.
See it visually