Invention Grant
- Patent Title: Learning robotic tasks using one or more neural networks
-
Application No.: US16255038Application Date: 2019-01-23
-
Publication No.: US11941719B2Publication Date: 2024-03-26
- Inventor: Jonathan Tremblay , Stan Birchfield , Stephen Tyree , Thang To , Jan Kautz , Artem Molchanov
- Applicant: Nvidia Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Davis Wright Tremaine LLP
- Main IPC: G06T9/00
- IPC: G06T9/00 ; B25J9/16 ; G05B13/00 ; G06N3/08 ; G06T1/00 ; G06T7/73 ; G05D1/00

Abstract:
Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
Public/Granted literature
- US20190228495A1 LEARNING ROBOTIC TASKS USING ONE OR MORE NEURAL NETWORKS Public/Granted day:2019-07-25
Information query