Definition
Grasp planning determines where and how a robot gripper should contact an object to achieve a stable grasp. Classical approaches use analytic methods (force closure, form closure) on known 3D models. Modern approaches use neural network grasp prediction from point clouds or depth images — models like GraspNet, Contact-GraspNet, and AnyGrasp predict 6-DOF grasp poses directly. Grasp planning intersects with motion planning (reaching the grasp pose collision-free) and is a prerequisite for most pick-and-place manipulation pipelines.
Why It Matters for Robot Teams
Understanding grasp planning is essential for teams building real-world robot systems. Whether you are collecting demonstration data, training policies in simulation, or deploying in production, this concept directly affects your workflow and system design.