Navigation & SLAM

Obstacle Avoidance

Obstacle avoidance is the capability of a robot to detect and steer around objects blocking its motion, spanning reactive local methods and deliberative planning. Classical techniques include artificial potential fields, the dynamic window approach, and velocity obstacles for moving agents, while manipulation stacks avoid obstacles through collision checking in motion planners. Learned policies increasingly handle avoidance implicitly from perception.

Why it matters for physical AI

Safe operation around people, clutter, and other robots is non-negotiable for deployment, making reliable avoidance a baseline requirement for both navigation and arm motion generation.