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    METHOD TO INCORPORATE UNCERTAIN INPUTS INTO NEURAL NETWORKS
    41.
    发明公开
    METHOD TO INCORPORATE UNCERTAIN INPUTS INTO NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230169329A1

    公开(公告)日：2023-06-01

    申请号：US17540107

    申请日：2021-12-01

    Applicant: NVIDIA Corporation

    Inventor： Fabio Tozeto Ramos ,  Rika Antonova ,  Ankur Handa ,  Dieter Fox

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Systems and methods related to incorporating uncertain inputs into a neural network are described herein. A distribution is obtained and processed by a Reproducing Kernel Hilbert Space (RKHS) module to generate an embedding that represents the distribution. The features of the embedding may correspond to a number of Random Fourier Features (RFFs). The embedding can be added to additional features to form an aggregate input for the neural network. The neural network then processes the aggregate input to generate an output based on, at least in part, the embedding of the distribution. In some embodiments, a simulation can be run to generate a distribution for a feature, where each simulator instance generates a different sample for the feature over a plurality of time steps of the simulation. In some embodiments, the output neural network can be used to control robotic systems, vehicles, or other systems.

    SEMANTIC REARRANGEMENT OF UNKNOWN OBJECTS FROM NATURAL LANGUAGE COMMANDS
    42.
    发明申请
    SEMANTIC REARRANGEMENT OF UNKNOWN OBJECTS FROM NATURAL LANGUAGE COMMANDS 有权

    公开(公告)号：US20230073154A1

    公开(公告)日：2023-03-09

    申请号：US17930349

    申请日：2022-09-07

    Applicant: NVIDIA Corporation

    Inventor： Christopher Jason Paxton ,  Weiyu Liu ,  Tucker Ryer Hermans ,  Dieter Fox

    IPC: G10L15/18 ,  B25J13/00 ,  G10L15/22

    Abstract: A robotic system is provided for performing rearrangement tasks guided by a natural language instruction. The system can include a number of neural networks used to determine a selected rearrangement of the objects in accordance with the natural language instruction. A target object predictor network processes a point cloud of the scene and the natural language instruction to identify a set of query objects that are to-be-rearranged. A language conditioned prior network processes the point cloud, natural language instruction, and the set of query objects to sample a distribution of rearrangements to generate a number of sets of pose offsets for the set of query objects. A discriminator network then processes the samples to generate scores for the samples. The samples may be refined until a score for at least one of the sample generated by the discriminator network is above a threshold value.

    SIMULATION OF TASKS USING NEURAL NETWORKS
    45.
    发明申请
    SIMULATION OF TASKS USING NEURAL NETWORKS 审中-公开

    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.

    Simulation of tasks using neural networks
    46.
    发明授权
    Simulation of tasks using neural networks 有权

    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.

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