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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
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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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
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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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
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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