US 12,151,374 B2
Reachable manifold and inverse mapping training for robots
Julien Perez, Grenoble (FR); and Seungsu Kim, Meylan (FR)
Assigned to NAVER CORPORATION, Gyeonggi-Do (KR)
Filed by NAVER CORPORATION, Gyeonggi-do (KR)
Filed on Sep. 28, 2021, as Appl. No. 17/487,595.
Claims priority of provisional application 63/165,906, filed on Mar. 25, 2021.
Prior Publication US 2022/0305649 A1, Sep. 29, 2022
Int. Cl. B25J 9/16 (2006.01); G05B 13/02 (2006.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01)
CPC B25J 9/1612 (2013.01) [B25J 9/161 (2013.01); B25J 9/1671 (2013.01); G05B 13/027 (2013.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01)] 27 Claims
OG exemplary drawing
 
1. A system for training a neural network to control movement of an end effector of a robot, the system comprising:
a first forward network module configured to, based on a set of target robot joint angles, generate a first estimated end effector pose and a first estimated latent variable that is a first intermediate variable between the set of target robot joint angles and the first estimated end effector pose;
an inverse network module configured to determine a set of estimated robot joint angles based on (a) the first estimated latent variable and (b) a target end effector pose;
a density network module configured to determine joint probabilities for the robot based on (a) the first estimated latent variable and (b) the target end effector pose; and
a second forward network module configured to, based on the set of estimated robot joint angles, determine (a) a second estimated end effector pose and (b) a second estimated latent variable that is a second intermediate variable between the set of estimated robot joint angles and the second estimated end effector pose,
wherein the joint probabilities define a reachable manifold of the end effector of the robot, and
wherein the reachable manifold defines whether a set of robot joint angles is kinematically feasible for reaching an end effector pose.