What is Grasping Data?
Grasping Data — images, depth maps or point clouds of objects, annotated with the gripper poses that would pick each one up.
A grasp label is a pose, not a bounding box: where the gripper should sit, at what angle, and how wide it must open. The classic academic set, Cornell, is small and still used as a benchmark — 885 RGB-D images of 240 objects at 640 by 480, labeled with roughly 5,100 positive grasp rectangles and 2,900 negative ones. Jacquard is the synthetic counterpart: more than 54,000 generated images with grasp points and poses.
The geometry underneath is force closure — a grasp that resists disturbance because its contact forces oppose each other. An antipodal two-finger grasp is the simple case: contact on opposite sides, along the line between the fingers. Datasets label which candidate poses satisfy this, which is why they are paired with depth or point clouds rather than RGB alone.
The failure that matters is not a failed pick but a delayed one — a grasp that looks successful and slips two seconds later. That is why a usable grasp set records the outcome and defines a hold period, not just the pose. Public sets cover tabletop objects and two-finger grippers; a gripper or object family they do not cover can be collected to order, and the narrow scope usually makes these projects faster than a full manipulation program.
Related terms
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Robot Gripper Data
Robot Gripper Data — episodes and signals tied to the end effector: width, force, vacuum pressure and whether the grasp held.
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Robot Manipulation Data
Robot Manipulation Data — episodes of a robot arm moving objects, recorded together with the commands that produced the motion.
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Tactile Sensor Data
Tactile Sensor Data — contact measurements from a robot's fingertips or skin: pressure, force and, in optical sensors, images of the contact itself.
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