公开(公告)号：US20240095527A1

公开(公告)日：2024-03-21

申请号：US18448049

申请日：2023-08-10

Applicant: NVIDIA CORPORATION

Inventor： Ankur HANDA ,  Gavriel STATE ,  Arthur David ALLSHIRE ,  Dieter FOX ,  Jean-Francois Victor LAFLECHE ,  Jingzhou LIU ,  Viktor MAKOVIICHUK ,  Yashraj Shyam NARANG ,  Aleksei Vladimirovich PETRENKO ,  Ritvik SINGH ,  Balakumar SUNDARALINGAM ,  Karl VAN WYK ,  Alexander ZHURKEVICH

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.

公开(公告)号：US20250083309A1

公开(公告)日：2025-03-13

申请号：US18646312

申请日：2024-04-25

Applicant: NVIDIA Corporation

Inventor： Nathan Donald RATLIFF ,  Karl VAN WYK ,  Ankur HANDA ,  Viktor MAKOVIICHUK ,  Yijie GUO ,  Jie XU ,  Tyler LUM ,  Balakumar SUNDARALINGAM ,  Jingzhou LIU

IPC: B25J9/16

Abstract: In various examples, systems and methods are disclosed relating to geometric fabrics for accelerated policy learning and sim-to-real transfer in robotics systems, platforms, and/or applications. For example, a system can provide an input indicative of a goal pose for a robot to a model to cause the model to generate an output, the output representing a plurality of points along a path for movement of the robot to the goal pose; and generate one or more control signals for operation of the robot based at least on the plurality of points along the path and a policy corresponding to one or more criteria for the operation of the robot. In examples, the system can provide the one or more control signals to the robot to cause the robot to move toward the goal pose.