公开(公告)号：US20200306960A1

公开(公告)日：2020-10-01

申请号：US16372274

申请日：2019-04-01

Applicant: NVIDIA Corporation

Inventor： Ankur Handa ,  Viktor Makoviichuk ,  Miles Macklin ,  Nathan Ratliff ,  Dieter Fox ,  Yevgen Chebotar ,  Jan Issac

IPC: B25J9/16 ,  G05B13/02 ,  G05B13/04 ,  G05D1/00

Abstract: A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.

公开(公告)号：US12275146B2

公开(公告)日：2025-04-15

申请号：US16372274

申请日：2019-04-01

Applicant: NVIDIA Corporation

Inventor： Ankur Handa ,  Viktor Makoviichuk ,  Miles Macklin ,  Nathan Ratliff ,  Dieter Fox ,  Yevgen Chebotar ,  Jan Issac

IPC: G05B13/04 ,  B25J9/16 ,  G05B13/02 ,  G05D1/00

Abstract: A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.