Navigation & SLAM

Collision Avoidance

Collision avoidance is the capability of a robot to prevent unwanted contact with obstacles, people, and itself while executing motion, implemented through planning around known geometry, reactive methods like dynamic window approach or artificial potential fields, and control-level safety filters such as control barrier functions. It spans static and dynamic obstacles and requires reliable perception of the environment, whether from depth sensors, lidar, or learned occupancy predictions.

Why it matters for physical AI

Learned policies do not inherently guarantee obstacle safety, so runtime avoidance layers and safety filters wrapped around neural policies are standard practice for deploying manipulation and navigation in unstructured spaces.