公开(公告)号：US20230339107A1

公开(公告)日：2023-10-26

申请号：US17940267

申请日：2022-09-08

Applicant: Honda Motor Co., Ltd.

Inventor： Ethan K. GORDON ,  Rana SOLTANI ZARRIN

IPC: B25J9/16 ,  B25J13/00

CPC classification number: B25J9/163 ,  B25J13/006

Abstract: Systems and methods for online augmentation for learned grasping are provided. In one embodiment, a method is provided that includes identifying an action from a discrete action space. The method includes identifying a second set of grasps of the agent utilizing a transition model based on the action and at least one contact parameter. The at least one contact parameter defines allowed states of contact for the agent. The method includes applying a reward function to evaluate each grasp of the second set of grasps based on a set of contact forces within a friction cone that minimizes a difference between an actual net wrench on the object and a predetermined net wrench. The reward function is optimized online using a lookahead tree. The method includes selecting a next grasp from the second set. The method includes causing the agent to execute the next grasp.

公开(公告)号：US20230316126A1

公开(公告)日：2023-10-05

申请号：US17950552

申请日：2022-09-22

Applicant: Honda Motor Co., Ltd.

Inventor： Ethan K. GORDON ,  Rana SOLTANI ZARRIN

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A system and method for providing accelerated reinforcement training that include receiving training data associated with a plurality of atomic actions. The system and method also include inputting the training data associated with the plurality of atomic actions to a neural network. The system and method additionally include completing dynamic programming to generate an optimal policy. The system and method further include inputting the optimal policy through a behavior cloning pipeline to output an expert policy for behavior cloning that is associated with the plurality of atomic actions.