US 12,390,929 B2
Object rearrangement using learned implicit collision functions
Michael Danielczuk, Berkeley, CA (US); Arsalan Mousavian, Seattle, WA (US); Clemens Eppner, Seattle, WA (US); and Dieter Fox, Seattle, WA (US)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Mar. 11, 2021, as Appl. No. 17/199,174.
Claims priority of provisional application 63/113,726, filed on Nov. 13, 2020.
Prior Publication US 2022/0152826 A1, May 19, 2022
Int. Cl. B25J 9/16 (2006.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G08G 1/16 (2006.01)
CPC B25J 9/1666 (2013.01) [B25J 9/1697 (2013.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G08G 1/166 (2013.01); G05B 2219/40477 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A method to detect whether a collision with an object in a scene will occur, the method comprising:
determining a set of object features based at least in part on a first point cloud associated with the object;
determining a set of scene features based at least in part on a second point cloud associated with the scene;
predicting a collision-free object path of the object within the scene at least by:
generating one or more queries based at least in part on the set of object features, the set of scene features, and one or more transforms associated with the object, the one or more queries to comprise one or more potential object paths through the scene and one or more object rotations along at least one of the one or more potential object paths; and
using at least one neural network to infer the collision-free object path, based at least in part on input of the one or more queries into the at least one neural network, wherein the at least one neural network comprises one or more classifier neural networks; and
causing at least one device to move the object along the collision-free object path from a first position to a second position.