Autonomous Object Learning by Robots Triggered by Remote Operators

    公开(公告)号:US20230158668A1

    公开(公告)日:2023-05-25

    申请号:US18158569

    申请日:2023-01-24

    Abstract: A method includes receiving, by a control system of a robotic device, data about an object in an environment from a remote computing device, where the data comprises at least location data and identifier data. The method further includes, based on the location data, causing at least one appendage of the robotic device to move through a predetermined learning motion path. The method additionally includes, while the at least one appendage moves through the predetermined learning motion path, causing one or more visual sensors to capture a plurality of images for potential association with the identifier data. The method further includes sending, to the remote computing device, the plurality of captured images to be displayed on a display interface of the remote computing device.

    Deformulation techniques for deducing the composition of a material from a spectrogram

    公开(公告)号:US11630057B2

    公开(公告)日:2023-04-18

    申请号:US17658765

    申请日:2022-04-11

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    DEFORMULATION TECHNIQUES FOR DEDUCING THE COMPOSITION OF A MATERIAL FROM A SPECTROGRAM

    公开(公告)号:US20220236171A1

    公开(公告)日:2022-07-28

    申请号:US17658765

    申请日:2022-04-11

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    DEFORMULATION TECHNIQUES FOR DEDUCING THE COMPOSITION OF A MATERIAL FROM A SPECTROGRAM

    公开(公告)号:US20220099566A1

    公开(公告)日:2022-03-31

    申请号:US16948760

    申请日:2020-09-30

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    RESOURCE EFFICIENT TRAINING OF MACHINE LEARNING MODELS THAT PREDICT STOCHASTIC SPREAD

    公开(公告)号:US20240233346A9

    公开(公告)日:2024-07-11

    申请号:US18493018

    申请日:2023-10-24

    CPC classification number: G06V10/776 G06V10/761

    Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

    Techniques for selection of light source configurations for material characterization

    公开(公告)号:US11781972B2

    公开(公告)日:2023-10-10

    申请号:US16949601

    申请日:2020-11-05

    CPC classification number: G01N21/255 G01N21/3563 G01N33/442 G06N3/126

    Abstract: Techniques for selecting a spectroscopic light source include obtaining a light source dataset and a spectroscopic dataset, initializing a genetic algorithm, selecting a first individual solution and a second individual solution from an initial generation of solutions, generating a new individual solution from the first and second individual solutions by combining their respective chromosome encodings, evaluating a specificity of the new individual solution to a target material, adding the new individual solution to a new generation of solutions, populating the new generation of solutions with a plurality of additional individual solutions, generating one or more descendent generations of solutions by iterating the genetic algorithm, selecting one or more implementation individual solutions exhibiting a threshold specificity to the target material, and outputting the one or more implementation individual solutions.

    Autonomous object learning by robots triggered by remote operators

    公开(公告)号:US11584004B2

    公开(公告)日:2023-02-21

    申请号:US16716874

    申请日:2019-12-17

    Abstract: A method includes receiving, by a control system of a robotic device, data about an object in an environment from a remote computing device, where the data comprises at least location data and identifier data. The method further includes, based on the location data, causing at least one appendage of the robotic device to move through a predetermined learning motion path. The method additionally includes, while the at least one appendage moves through the predetermined learning motion path, causing one or more visual sensors to capture a plurality of images for potential association with the identifier data. The method further includes sending, to the remote computing device, the plurality of captured images to be displayed on a display interface of the remote computing device.

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