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公开(公告)号：US11727593B1
公开(公告)日：2023-08-15
申请号：US17397296
申请日：2021-08-09
申请人： X Development LLC
IPC分类号： G06T7/73 , G06T7/60 , G06N3/08 , G06F16/583 , G06V10/42 , G06V10/46 , G06V30/194 , G06F18/214
CPC分类号： G06T7/73 , G06F16/5854 , G06F18/214 , G06N3/08 , G06T7/60 , G06V10/42 , G06V10/462 , G06V30/194 , G06T2200/04 , G06T2207/30244 , G06T2219/004
摘要： Methods for annotating objects within image frames are disclosed. Information is obtained that represents a camera pose relative to a scene. The camera pose includes a position and a location of the camera relative to the scene. Data is obtained that represents multiple images, including a first image and a plurality of other images, being captured from different angles by the camera relative to the scene. A 3D pose of the object of interest is identified with respect to the camera pose in at least the first image. A 3D bounding region for the object of interest in the first image is defined, which indicates a volume that includes the object of interest. A location and orientation of the object of interest is determined in the other images based on the defined 3D bounding region of the object of interest and the camera pose in the other images.
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公开(公告)号：US20210229276A1
公开(公告)日：2021-07-29
申请号：US17230628
申请日：2021-04-14
申请人： X Development LLC
摘要： Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.
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公开(公告)号：US20200167606A1
公开(公告)日：2020-05-28
申请号：US16692509
申请日：2019-11-22
申请人： X Development LLC
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.
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公开(公告)号：US11314987B2
公开(公告)日：2022-04-26
申请号：US16692509
申请日：2019-11-22
申请人： X Development LLC
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.
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公开(公告)号：US10417781B1
公开(公告)日：2019-09-17
申请号：US15396105
申请日：2016-12-30
申请人： X Development LLC
摘要： Methods for annotating objects within image frames are disclosed. Information is obtained that represents a camera pose relative to a scene. The camera pose includes a position and a location of the camera relative to the scene. Data is obtained that represents multiple images, including a first image and a plurality of other images, being captured from different angles by the camera relative to the scene. A 3D pose of the object of interest is identified with respect to the camera pose in at least the first image. A 3D bounding region for the object of interest in the first image is defined, which indicates a volume that includes the object of interest. A location and orientation of the object of interest is determined in the other images based on the defined 3D bounding region of the object of interest and the camera pose in the other images.
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公开(公告)号：US11607807B2
公开(公告)日：2023-03-21
申请号：US17230628
申请日：2021-04-14
申请人： X Development LLC
摘要： Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.
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公开(公告)号：US20220215208A1
公开(公告)日：2022-07-07
申请号：US17656137
申请日：2022-03-23
申请人： X Development LLC
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.
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公开(公告)号：US11151744B1
公开(公告)日：2021-10-19
申请号：US16571841
申请日：2019-09-16
申请人： X Development LLC
摘要： Methods for annotating objects within image frames are disclosed. Information is obtained that represents a camera pose relative to a scene. The camera pose includes a position and a location of the camera relative to the scene. Data is obtained that represents multiple images, including a first image and a plurality of other images, being captured from different angles by the camera relative to the scene. A 3D pose of the object of interest is identified with respect to the camera pose in at least the first image. A 3D bounding region for the object of interest in the first image is defined, which indicates a volume that includes the object of interest. A location and orientation of the object of interest is determined in the other images based on the defined 3D bounding region of the object of interest and the camera pose in the other images.
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公开(公告)号：US11007642B2
公开(公告)日：2021-05-18
申请号：US16167596
申请日：2018-10-23
申请人： X Development LLC
摘要： Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.
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