Control

Singularity Avoidance

Singularity avoidance is the set of techniques for keeping a manipulator away from, or behaving stably near, singular configurations. Common methods include damped least-squares (Levenberg-Marquardt) inverse kinematics that trades tracking accuracy for bounded joint velocities, manipulability-maximizing null-space motion for redundant arms, and planners that treat low-manipulability regions as obstacles. Singularity-robust inverse kinematics is standard in industrial and research control stacks.

Why it matters for physical AI

Learned policies that output end-effector targets rely on the underlying IK layer to survive singular regions, making robust inversion a quiet prerequisite for safe deployment.