Manipulation

Sensor-Based Grasping

Sensor-based grasping is grasp execution guided by real-time sensor feedback — vision, tactile arrays, force-torque sensing, or proximity sensors — rather than open-loop replay of a preplanned grasp pose. Closed-loop approaches adjust the approach and finger closure as new observations arrive, correcting for perception error, object movement, and slip. QT-Opt (2018), which learned closed-loop vision-based grasping from over 500,000 real grasp attempts, is a landmark example.

Why it matters for physical AI

Open-loop grasps fail whenever calibration drifts or objects shift; feedback-driven grasping is what makes picking robust in cluttered, dynamic settings like bins and homes.