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公开(公告)号：US20240256865A1
公开(公告)日：2024-08-01
申请号：US18430586
申请日：2024-02-01
Applicant: Google LLC
Inventor： Deepali Jain , Krzysztof Marcin Choromanski , Sumeet Singh , Vikas Sindhwani , Tingnan Zhang , Jie Tan , Kumar Avinava Dubey
IPC: G06N3/08 , G06N3/0455
CPC classification number: G06N3/08 , G06N3/0455
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks. One of the methods for training a neural network configured to perform a machine learning task includes performing, at each of a plurality of iterations: performing a training step to obtain respective new gradients of a loss function; for each network parameter: generating an optimizer network input; processing the optimizer network input using an optimizer neural network, wherein the processing comprises, for each cell: generating a cell input for the cell; and processing the cell input for the cell to generate a cell output, wherein the processing comprises: obtaining latent embeddings from the cell input; generating the cell output from the hidden state; and determining an update to the hidden state; and generating an optimizer network output defining an update for the network parameter; and applying the update to the network parameter.
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公开(公告)号：US11992945B2
公开(公告)日：2024-05-28
申请号：US17094521
申请日：2020-11-10
Applicant: Google LLC
Inventor： Jie Tan , Sehoon Ha , Peng Xu , Sergey Levine , Zhenyu Tan
CPC classification number: B25J9/163 , B25J9/162 , B25J9/1689 , B25J13/089 , G05D1/02 , G06N3/08
Abstract: Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.
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公开(公告)号：US11328446B2
公开(公告)日：2022-05-10
申请号：US15635894
申请日：2017-06-28
Applicant: Google LLC
Inventor： Jie Tan , Gang Pan , Jon Karafin , Thomas Nonn , Julio C. Hernandez Zaragoza
IPC: G06T7/80 , G06T3/00 , G06T7/521 , G06K9/46 , G01S17/08 , G01S17/42 , H04N13/271 , G01S17/86 , H04N13/232
Abstract: Depths of one or more objects in a scene may be measured with enhanced accuracy through the use of a light-field camera and a depth sensor. The light-field camera may capture a light-field image of the scene. The depth sensor may capture depth sensor data of the scene. Light-field depth data may be extracted from the light-field image and used, in combination with the sensor depth data, to generate a depth map indicative of distance between the light-field camera and one or more objects in the scene. The depth sensor may be an active depth sensor that transmits electromagnetic energy toward the scene; the electromagnetic energy may be reflected off of the scene and detected by the active depth sensor. The active depth sensor may have a 360° field of view; accordingly, one or more mirrors may be used to direct the electromagnetic energy between the active depth sensor and the scene.
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公开(公告)号：US20210182620A1
公开(公告)日：2021-06-17
申请号：US16717471
申请日：2019-12-17
Applicant: Google LLC
Inventor： Jie Tan , Sehoon Ha , Tingnan Zhang , Xinlei Pan , Brian Andrew Ichter , Aleksandra Faust
Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.
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公开(公告)号：US20220143819A1
公开(公告)日：2022-05-12
申请号：US17094521
申请日：2020-11-10
Applicant: Google LLC
Inventor： Jie Tan , Sehoon Ha , Peng Xu , Sergey Levine , Zhenyu Tan
Abstract: Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.
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Patent Agency Ranking
公开(公告)号：US10275892B2
公开(公告)日：2019-04-30
申请号：US15462752
申请日：2017-03-17
Applicant: Google LLC
Inventor： Francois Bleibel , Tingfang Du , Thomas Nonn , Jie Tan
Abstract: A depth-based effect may be applied to a multi-view video stream to generate a modified multi-view video stream. User input may designate a boundary between a foreground region and a background region, at a different depth from the foreground region, of a reference image of the video stream. Based on the user input, a reference mask may be generated to indicate the foreground region and the background region. The reference mask may be used to generate one or more other masks that indicate the foreground and background regions for one or more different images, from different frames and/or different views from the reference image. The reference mask and other mask(s) may be used to apply the effect to the multi-view video stream to generate the modified multi-view video stream.
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公开(公告)号：US12172309B2
公开(公告)日：2024-12-24
申请号：US17047892
申请日：2019-04-22
Applicant: Google LLC
Inventor： Jie Tan , Tingnan Zhang , Atil Iscen , Erwin Coumans , Yunfei Bai
IPC: B62D57/032 , B25J9/16 , G05D1/00 , G06N3/042 , G06N3/08
Abstract: Training and/or using a machine learning model for locomotion control of a robot, where the model is decoupled. In many implementations, the model is decoupled into an open loop component and a feedback component, where a user can provide a desired reference trajectory (e.g., a symmetric sine curve) as input for the open loop component. In additional and/or alternative implementations, the model is decoupled into a pattern generator component and a feedback component, where a user can provide controlled parameter(s) as input for the pattern generator component to generate pattern generator phase data (e.g., an asymmetric sine curve). The neural network model can be used to generate robot control parameters.
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公开(公告)号：US11436441B2
公开(公告)日：2022-09-06
申请号：US16717471
申请日：2019-12-17
Applicant: Google LLC
Inventor： Jie Tan , Sehoon Ha , Tingnan Zhang , Xinlei Pan , Brian Andrew Ichter , Aleksandra Faust
Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.
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公开(公告)号：US20210162589A1
公开(公告)日：2021-06-03
申请号：US17047892
申请日：2019-04-22
Applicant: Google LLC
Inventor： Jie Tan , Tingnan Zhang , Atil Iscen , Erwin Coumans , Yunfei Bai
IPC: B25J9/16 , G05D1/08 , B62D57/032 , G06N3/04 , G06N3/08
Abstract: Training and/or using a machine learning model for locomotion control of a robot, where the model is decoupled. In many implementations, the model is decoupled into an open loop component and a feedback component, where a user can provide a desired reference trajectory (e.g., a symmetric sine curve) as input for the open loop component. In additional and/or alternative implementations, the model is decoupled into a pattern generator component and a feedback component, where a user can provide controlled parameter(s) as input for the pattern generator component to generate pattern generator phase data (e.g., an asymmetric sine curve). The neural network model can be used to generate robot control parameters.
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公开(公告)号：US10545215B2
公开(公告)日：2020-01-28
申请号：US15703553
申请日：2017-09-13
Applicant: Google LLC
Inventor： Jon Karafin , Gang Pan , Thomas Nonn , Jie Tan
Abstract: A light-field video stream may be processed to modify the camera pathway from which the light-field video stream is projected. A plurality of target pixels may be selected, in a plurality of key frames of the light-field video stream. The target pixels may be used to generate a camera pathway indicative of motion of the camera during generation of the light-field video stream. The camera pathway may be adjusted to generate an adjusted camera pathway. This may be done, for example, to carry out image stabilization. The light-field video stream may be projected to a viewpoint defined by the adjusted camera pathway to generate a projected video stream with the image stabilization.
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