Manipulation

Grasp Planning

Grasp planning is the problem of selecting where and how a robot hand should grasp an object, producing contact points or a gripper pose together with an approach motion, subject to stability criteria, hand kinematics, reachability, collisions, and task requirements. Classical analytic planners optimized force-closure metrics over object models, as in the GraspIt! simulator, while modern systems dominantly use learned grasp detection combined with motion planning for approach and retrieval.

Why it matters for physical AI

A grasp is the precondition for nearly every manipulation task, and planning failures cascade downstream; the field's move from analytic models to learned prediction previews the broader arc of physical AI.