AUTONOMOUS VEHICLE SIMULATION USING MACHINE LEARNING

    公开(公告)号:US20200368906A1

    公开(公告)日:2020-11-26

    申请号:US16417540

    申请日:2019-05-20

    Abstract: In an embodiment, a system calculates a distribution of possible parameters for a simulation that cause the simulation to match a measured behavior in the real world. In an embodiment, the system selects a plurality of simulation parameters based on a statistical distribution that represents an initial estimate of possible parameter values. In an embodiment, using the results produced by the simulation, an updated distribution of possible parameters is constructed based on a density of the results modeled using Fourier features. In an embodiment, the updated distribution of possible parameters can be used to select a particular set of parameters for the simulation, which cause the simulator approximate the measured behavior.

    DIFFERENTIABLE SIMULATOR FOR ROBOTIC CUTTING

    公开(公告)号:US20220382246A1

    公开(公告)日:2022-12-01

    申请号:US17732313

    申请日:2022-04-28

    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.

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