Machine learning control of object handovers
AssigneeNVIDIA Corporation
InventorsWei Yang, Christopher Jason Paxton, Yu-Wei Chao, Dieter Fox
Life of this patent
Estimating the term…
Abstract
A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.
Claims20
A processor, comprising one or more circuits to:
- generate a plurality of grasp poses that allow a robot to grasp an object;
- and
- select, based at least in part on a pose of a hand, a target grasp pose from the plurality of grasp poses that does not interfere with the hand.
A system, comprising one or more processors to:
- generate a plurality of grasp poses that allow a robot to grasp an object;
- and
- select, based at least in part on a pose of a hand, a target grasp pose from the plurality of grasp poses that does not interfere with the hand.
A non-transitory machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least:
- generate a plurality of grasp poses that allow a robot to grasp an object;
- and
- select, based at least in part on a pose of a hand, a target grasp pose from the plurality of grasp poses that does not interfere with the hand.
Dependencies are inferred from the claim text.