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1.发明授权公开(公告)号：US10766136B1
公开(公告)日：2020-09-08
申请号：US15803610
申请日：2017-11-03
发明人： Brandon William Porter , Leonardo Ruggiero Bachega , Brian C. Beckman , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
摘要： A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.
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公开(公告)号：US10792810B1
公开(公告)日：2020-10-06
申请号：US15842707
申请日：2017-12-14
发明人： Brian C. Beckman , Leonardo Ruggiero Bachega , Brandon William Porter , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
摘要： A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
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公开(公告)号：US10800040B1
公开(公告)日：2020-10-13
申请号：US15842737
申请日：2017-12-14
发明人： Brian C. Beckman , Leonardo Ruggiero Bachega , Brandon William Porter , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
IPC分类号： B25J9/16
摘要： A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
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4.发明授权公开(公告)号：US10766137B1
公开(公告)日：2020-09-08
申请号：US15803621
申请日：2017-11-03
发明人： Brandon William Porter , Leonardo Ruggiero Bachega , Brian C. Beckman , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
摘要： A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.
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公开(公告)号：US11584008B1
公开(公告)日：2023-02-21
申请号：US17067525
申请日：2020-10-09
发明人： Brian C. Beckman , Leonardo Ruggiero Bachega , Brandon William Porter , Benjamin Lev Snyder , Michael Vogelsong , Corrinne Yu
IPC分类号： B25J9/16
摘要： A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.
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6.发明授权公开(公告)号：US11188831B2
公开(公告)日：2021-11-30
申请号：US15796410
申请日：2017-10-27
发明人： Benjamin Lev Snyder , Liliane Jeanne Barbour , Aritra Biswas , Simone Elviretti , Rasika Sanjay Jangle , Paul Hercules Mandac Rivera , James Stevenson , Daniel Patrick Weaver
IPC分类号： G06N5/02 , G06F16/54 , G06Q30/06 , G06F16/904 , G06F16/9038 , G06N3/04
摘要： In response to a programmatic interaction, respective representations of items of an initial result set are presented to an item consumer. One or more result refinement iterations are then conducted. In a given iteration, one or more feedback indicators with respect to one or more items are identified, a machine learning model is trained using at least the feedback indicators to generate respective result set candidacy metrics for at least some items, and the metrics are then used to transmit additional items for presentation to the item consumer.
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7.发明申请公开(公告)号：US20190130285A1
公开(公告)日：2019-05-02
申请号：US15796410
申请日：2017-10-27
发明人： Benjamin Lev Snyder , Liliane Jeanne Barbour , Aritra Biswas , Simone Elviretti , Rasika Sanjay Jangle , Paul Hercules Mandac Rivera , James Stevenson , Daniel Patrick Weaver
摘要： In response to a programmatic interaction, respective representations of items of an initial result set are presented to an item consumer. One or more result refinement iterations are then conducted. In a given iteration, one or more feedback indicators with respect to one or more items are identified, a machine learning model is trained using at least the feedback indicators to generate respective result set candidacy metrics for at least some items, and the metrics are then used to transmit additional items for presentation to the item consumer.
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