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公开(公告)号：US11514293B2
公开(公告)日：2022-11-29
申请号：US16564978
申请日：2019-09-09
Applicant: NVIDIA Corporation
Inventor： Ruben Villegas , Alejandro Troccoli , Iuri Frosio , Stephen Tyree , Wonmin Byeon , Jan Kautz
Abstract: In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used—e.g., by a bi-directional long short-term memory (LSTM) network—to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects—according to trajectories determined from the future locations—through a simulation environment.
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公开(公告)号：US20190228495A1
公开(公告)日：2019-07-25
申请号：US16255038
申请日：2019-01-23
Applicant: Nvidia Corporation
Inventor： Jonathan Tremblay , Stan Birchfield , Stephen Tyree , Thang To , Jan Kautz , Artem Molchanov
Abstract: Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
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公开(公告)号：US20230088912A1
公开(公告)日：2023-03-23
申请号：US17952866
申请日：2022-09-26
Applicant: NVIDIA Corporation
Inventor： Ruben Villegas , Alejandro Troccoli , Iuri Frosio , Stephen Tyree , Wonmin Byeon , Jan Kautz
Abstract: In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used—e.g., by a bi-directional long short-term memory (LSTM) network—to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects—according to trajectories determined from the future locations—through a simulation environment.
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公开(公告)号：US20210390653A1
公开(公告)日：2021-12-16
申请号：US17458221
申请日：2021-08-26
Applicant: Nvidia Corporation
Inventor： Jonathan Tremblay , Stan Birchfield , Stephen Tyree , Thang To , Jan Kautz , Artem Molchanov
Abstract: Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
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公开(公告)号：US11989642B2
公开(公告)日：2024-05-21
申请号：US17952866
申请日：2022-09-26
Applicant: NVIDIA Corporation
Inventor： Ruben Villegas , Alejandro Troccoli , Iuri Frosio , Stephen Tyree , Wonmin Byeon , Jan Kautz
Abstract: In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used—e.g., by a bi-directional long short-term memory (LSTM) network—to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects—according to trajectories determined from the future locations—through a simulation environment.
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Patent Agency Ranking
公开(公告)号：US20210124353A1
公开(公告)日：2021-04-29
申请号：US17140738
申请日：2021-01-04
Applicant: Nvidia Corporation
Inventor： Bill Dally , Stephen Tyree , Iuri Frosio , Alejandro Troccoli
Abstract: Sensors measure information about actors or other objects near an object, such as a vehicle or robot, to be maneuvered. Sensor data is used to determine a sequence of possible actions for the maneuverable object to achieve a determined goal. For each possible action to be considered, one or more probable reactions of the nearby actors or objects are determined. This can take the form of a decision tree in some embodiments, with alternative levels of nodes corresponding to possible actions of the present object and probable reactive actions of one or more other vehicles or actors. Machine learning can be used to determine the probabilities, as well as to project out the options along the paths of the decision tree including the sequences. A value function is used to generate a value for each considered sequence, or path, and a path having a highest value is selected for use in determining how to navigate the object.
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公开(公告)号：US20200082248A1
公开(公告)日：2020-03-12
申请号：US16564978
申请日：2019-09-09
Applicant: NVIDIA Corporation
Inventor： Ruben Villegas , Alejandro Troccoli , Iuri Frosio , Stephen Tyree , Wonmin Byeon , Jan Kautz
Abstract: In various examples, historical trajectory information of objects in an environment may be tracked by an ego-vehicle and encoded into a state feature. The encoded state features for each of the objects observed by the ego-vehicle may be used—e.g., by a bi-directional long short-term memory (LSTM) network—to encode a spatial feature. The encoded spatial feature and the encoded state feature for an object may be used to predict lateral and/or longitudinal maneuvers for the object, and the combination of this information may be used to determine future locations of the object. The future locations may be used by the ego-vehicle to determine a path through the environment, or may be used by a simulation system to control virtual objects—according to trajectories determined from the future locations—through a simulation environment.
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公开(公告)号：US20240127075A1
公开(公告)日：2024-04-18
申请号：US18212629
申请日：2023-06-21
Applicant: NVIDIA Corporation
Inventor： Shalini De Mello , Christian Jacobsen , Xunlei Wu , Stephen Tyree , Alice Li , Wonmin Byeon , Shangru Li
IPC: G06N3/0985
CPC classification number: G06N3/0985
Abstract: Machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. In order to reduce the costs associated with collecting and labeling real world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real world data. The present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).
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公开(公告)号：US11941719B2
公开(公告)日：2024-03-26
申请号：US16255038
申请日：2019-01-23
Applicant: Nvidia Corporation
Inventor： Jonathan Tremblay , Stan Birchfield , Stephen Tyree , Thang To , Jan Kautz , Artem Molchanov
CPC classification number: G06T1/0014 , B25J9/161 , B25J9/1661 , B25J9/1697 , G05B13/00 , G06N3/08 , G06T7/73 , G05D1/0088 , G05D1/0221 , G05D2201/0213 , G06T2207/20081 , G06T2207/20084
Abstract: Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
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公开(公告)号：US20200249674A1
公开(公告)日：2020-08-06
申请号：US16268188
申请日：2019-02-05
Applicant: Nvidia Corporation
Inventor： Bill Dally , Stephen Tyree , Iuri Frosio , Alejandro Troccoli
Abstract: Sensors measure information about actors or other objects near an object, such as a vehicle or robot, to be maneuvered. Sensor data is used to determine a sequence of possible actions for the maneuverable object to achieve a determined goal. For each possible action to be considered, one or more probable reactions of the nearby actors or objects are determined. This can take the form of a decision tree in some embodiments, with alternative levels of nodes corresponding to possible actions of the present object and probable reactive actions of one or more other vehicles or actors. Machine learning can be used to determine the probabilities, as well as to project out the options along the paths of the decision tree including the sequences. A value function is used to generate a value for each considered sequence, or path, and a path having a highest value is selected for use in determining how to navigate the object.
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