Control

State Estimation

State estimation is the inference of a system's internal state — pose, velocity, contact status, or joint configuration — from noisy, partial sensor measurements and a dynamics model. The Kalman filter and its extended and unscented variants handle Gaussian settings, particle filters address multimodal posteriors, and factor graph smoothers fuse measurements over trajectories. Legged robots fuse IMU, kinematics, and contact events to estimate base motion; manipulators estimate object pose and contact forces.

Why it matters for physical AI

Controllers and policies act on estimates, not truth; estimator bias or lag propagates directly into instability and failed grasps, making estimation quality a hard floor on performance.