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    Automatic robot perception programming by imitation learning
    1.
    发明授权
    Automatic robot perception programming by imitation learning 有权

    公开(公告)号：US11577388B2

    公开(公告)日：2023-02-14

    申请号：US16455190

    申请日：2019-06-27

    Applicant: Intel Corporation

    Inventor： David I. Gonzalez Aguirre ,  Javier Felip Leon ,  Javier Sebastián Turek ,  Luis Carlos Maria Remis ,  Ignacio Javier Alvarez ,  Justin Gottschlich

    IPC: B25J9/16

    Abstract: Apparatus, systems, methods, and articles of manufacture for automatic robot perception programming by imitation learning are disclosed. An example apparatus includes a percept mapper to identify a first percept and a second percept from data gathered from a demonstration of a task and an entropy encoder to calculate a first saliency of the first percept and a second saliency of the second percept. The example apparatus also includes a trajectory mapper to map a trajectory based on the first percept and the second percept, the first percept skewed based on the first saliency, the second percept skewed based on the second saliency. In addition, the example apparatus includes a probabilistic encoder to determine a plurality of variations of the trajectory and create a collection of trajectories including the trajectory and the variations of the trajectory. The example apparatus also includes an assemble network to imitate an action based on a first simulated signal from a first neural network of a first modality and a second simulated signal from a second neural network of a second modality, the action representative of a perceptual skill.

    ANALYTIC IMAGE FORMAT FOR VISUAL COMPUTING
    8.
    发明申请
    ANALYTIC IMAGE FORMAT FOR VISUAL COMPUTING 有权

    公开(公告)号：US20220180651A1

    公开(公告)日：2022-06-09

    申请号：US17374217

    申请日：2021-07-13

    Applicant: Intel Corporation

    Inventor： Christina R. Strong ,  Vishakha Gupta ,  Luis Carlos Maria Remis ,  Kushal Datta ,  Arun Raghunath

    IPC: G06V30/262 ,  G06V10/96 ,  G06V20/00 ,  G06T7/11 ,  G06K9/62

    Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

    EFFICIENT STORAGE AND PROCESSING OF HIGH-DIMENSIONAL FEATURE VECTORS
    9.
    发明申请
    EFFICIENT STORAGE AND PROCESSING OF HIGH-DIMENSIONAL FEATURE VECTORS 有权

    公开(公告)号：US20220164384A1

    公开(公告)日：2022-05-26

    申请号：US17362554

    申请日：2021-06-29

    Applicant: Intel Corporation

    Inventor： Luis Carlos Maria Remis ,  Vishakha Gupta ,  Christina R. Strong ,  Philip R. Lantz

    IPC: G06F16/901 ,  G06F16/903 ,  G06K9/62 ,  G06F16/22 ,  G06F16/2455

    Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a feature vector index, wherein the feature vector index comprises a sparse-array data structure representing a feature space for a set of labeled feature vectors, wherein the set of labeled feature vectors are assigned to a plurality of classes. The processor is to: receive a query corresponding to a target feature vector; access, via the storage device, a first portion of the feature vector index, wherein the first portion of the feature vector index comprises a subset of labeled feature vectors that correspond to a same portion of the feature space as the target feature vector; determine the corresponding class of the target feature vector based on the subset of labeled feature vectors; and provide a response to the query based on the corresponding class.

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