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公开(公告)号：US20190001489A1
公开(公告)日：2019-01-03
申请号：US15640914
申请日：2017-07-03
Applicant: X Development LLC
Inventor： Nicolas Hudson , Devesh Yamparala
CPC classification number: B25J9/161 , B25J9/1602 , B25J9/163 , B25J9/1656 , B25J9/1697 , G05B13/027 , G05B2219/33036 , G05B2219/33037 , G06N3/008 , G06N3/0454 , G06N3/084
Abstract: Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.
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公开(公告)号：US10853646B1
公开(公告)日：2020-12-01
申请号：US16453145
申请日：2019-06-26
Applicant: X Development LLC
Inventor： Adrian Li , Nicolas Hudson , Aaron Edsinger
Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating spatial affordances for an object, in an environment of a robot, and utilizing the generated spatial affordances in one or more robotics applications directed to the object. Various implementations relate to applying vision data as input to a trained machine learning model, processing the vision data using the trained machine learning model to generate output defining one or more spatial affordances for an object captured by the vision data, and controlling one or more actuators of a robot based on the generated output. Various implementations additionally or alternatively relate to training such a machine learning model.
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公开(公告)号：US10562181B2
公开(公告)日：2020-02-18
申请号：US15640914
申请日：2017-07-03
Applicant: X Development LLC
Inventor： Nicolas Hudson , Devesh Yamparala
Abstract: Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.
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公开(公告)号：US10354139B1
公开(公告)日：2019-07-16
申请号：US15724067
申请日：2017-10-03
Applicant: X Development LLC
Inventor： Adrian Li , Nicolas Hudson , Aaron Edsinger
Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating spatial affordances for an object, in an environment of a robot, and utilizing the generated spatial affordances in one or more robotics applications directed to the object. Various implementations relate to applying vision data as input to a trained machine learning model, processing the vision data using the trained machine learning model to generate output defining one or more spatial affordances for an object captured by the vision data, and controlling one or more actuators of a robot based on the generated output. Various implementations additionally or alternatively relate to training such a machine learning model.
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