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    Imitation learning system
    3.
    发明授权
    Imitation learning system 有权

    公开(公告)号：US11893468B2

    公开(公告)日：2024-02-06

    申请号：US16931211

    申请日：2020-07-16

    Applicant: NVIDIA Corporation

    Inventor： Yu-Wei Chao ,  De-An Huang ,  Christopher Jason Paxton ,  Animesh Garg ,  Dieter Fox

    IPC: G06N3/008 ,  G06N20/00

    CPC classification number: G06N3/008 ,  G06N20/00

    Abstract: Apparatuses, systems, and techniques to identify a goal of a demonstration. In at least one embodiment, video data of a demonstration is analyzed to identify a goal. Object trajectories identified in the video data are analyzed with respect to a task predicate satisfied by a respective object trajectory, and with respect to motion predicate. Analysis of the trajectory with respect to the motion predicate is used to assess intentionality of a trajectory with respect to the goal.

    DIFFERENTIABLE SIMULATOR FOR ROBOTIC CUTTING
    6.
    发明申请
    DIFFERENTIABLE SIMULATOR FOR ROBOTIC CUTTING 有权

    公开(公告)号：US20220382246A1

    公开(公告)日：2022-12-01

    申请号：US17732313

    申请日：2022-04-28

    Applicant: NVIDIA Corporation

    Inventor： Eric Heiden ,  Fabio Tozeto Ramos ,  Yashraj Narang ,  Miles Macklin ,  Dieter Fox ,  Animesh Garg ,  Mike Skolones

    IPC: G05B19/4069 ,  G06T17/20 ,  G05B19/416 ,  B25J9/16

    Abstract: A differentiable simulator for simulating the cutting of soft materials by a cutting instrument is provided. In accordance with one aspect of the disclosure, a method for simulating a cutting operation includes: receiving a mesh for an object, modifying the mesh to add virtual nodes associated with a predefined cutting plane, optimizing a set of parameters associated with a simulator based on ground-truth data, and running a simulation via the simulator to generate outputs that include trajectories associated with a cutting instrument. Optimizing the set of parameters can include performing inference based on a set of ground-truth trajectories captured using sensors to measure real-world cutting operations. The inference techniques can employ stochastic gradient descent, stochastic gradient Langevin dynamics, or a Bayesian approach. In an embodiment, the simulator can be utilized to generate control signals for a robot based on the simulated trajectories.

    FORCE ESTIMATION USING DEEP LEARNING
    10.
    发明申请
    FORCE ESTIMATION USING DEEP LEARNING 审中-公开

    公开(公告)号：US20200301510A1

    公开(公告)日：2020-09-24

    申请号：US16358485

    申请日：2019-03-19

    Applicant: NVIDIA Corporation

    Inventor： Stan Birchfield ,  Byron Boots ,  Dieter Fox ,  Ankur Handa ,  Nathan Ratliff ,  Balakumar Sundaralingam ,  Alexander Lambert

    IPC: G06F3/01 ,  G01L5/22 ,  G06N3/08 ,  G06T11/00

    Abstract: A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.

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