Generating directives for objective-effectuators

    公开(公告)号:US11436813B2

    公开(公告)日:2022-09-06

    申请号:US17325454

    申请日:2021-05-20

    Applicant: Apple Inc.

    Abstract: A method includes generating, in coordination with an emergent content engine, a first objective for a first objective-effectuator and a second objective for a second objective-effectuator instantiated in a computer-generated reality (CGR) environment. The first and second objectives are associated with a mutual plan. The method includes generating, based on characteristic values associated with the first and second objective-effectuators a first directive for the first objective-effectuator and a second directive for the second objective-effectuator. The first directive limits actions generated by the first objective-effectuator over a first set of time frames associated with the first objective and the second directive limits actions generated by the second objective-effectuator over a second set of time frames associated with the second objective. The method includes displaying manipulations of CGR representations of the first and second objective-effectuators in the CGR environment in accordance with the first and second directives.

    Planner for an objective-effectuator

    公开(公告)号:US11302080B1

    公开(公告)日:2022-04-12

    申请号:US16862936

    申请日:2020-04-30

    Applicant: Apple Inc.

    Abstract: In some implementations, a method includes obtaining an objective for a computer-generated reality (CGR) representation of an objective-effectuator. In some implementations, the objective is associated with a plurality of time frames. In some implementations, the method includes determining a plurality of candidate plans that satisfy the objective. In some implementations, the method includes selecting a first candidate plan of the plurality of candidate plans based on a selection criterion. In some implementations, the method includes effectuating the first candidate plan in order to satisfy the objective. In some implementations, the first candidate plan triggers the CGR representation of the objective-effectuator to perform a series of actions over the plurality of time frames associated with the objective.

    Training a Model with Human-Intuitive Inputs

    公开(公告)号:US20210374615A1

    公开(公告)日:2021-12-02

    申请号:US17397839

    申请日:2021-08-09

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating environment states is performed by a device including one or more processors and non-transitory memory. The method includes displaying an environment including an asset associated with a neural network model and having a plurality of asset states. The method includes receiving a user input indicative of a training request. The method includes selecting, based on the user input, a training focus indicating one or more of the plurality of asset states. The method includes generating a set of training data including a plurality of training instances weighted according to the training focus. The method includes training the neural network model on the set of training data.

    Training a character through interactions

    公开(公告)号:US12056581B1

    公开(公告)日:2024-08-06

    申请号:US16786097

    申请日:2020-02-10

    Applicant: Apple Inc.

    CPC classification number: G06N20/00 G06N3/006 G06N5/022 G06N5/04 G06N7/01

    Abstract: Various implementations disclosed herein include devices, systems, and methods for training of an action determining component of a computer character. In some implementations, actions are taken by the character in a 3D environment according to an action determining component of the character, where the character is rewarded or penalized for interactions associated with an object/concept in the 3D environment according to an assigned object/concept reward or penalty. In some implementations, the reward or the penalty assigned to the object/concept is modified, and the character is then rewarded or penalized for interactions associated with the object/concept according to the modified reward or the modified penalty. The action determining component of the character is trained using a reinforcement learning technique that accounts for rewards or penalties obtained by virtual character for interactions associated with the object/concept.

    Perceptual property vector for an object

    公开(公告)号:US11961191B2

    公开(公告)日:2024-04-16

    申请号:US17465320

    申请日:2021-09-02

    Applicant: Apple Inc.

    CPC classification number: G06T19/006 G06T15/04 G06T15/08 G06V20/41

    Abstract: In some implementations, a method includes obtaining a semantic construction of a physical environment. In some implementations, the semantic construction of the physical environment includes a representation of a physical element and a semantic label for the physical element. In some implementations, the method includes obtaining a graphical representation of the physical element. In some implementations, the method includes synthesizing a perceptual property vector (PPV) for the graphical representation of the physical element based on the semantic label for the physical element. In some implementations, the PPV includes one or more perceptual characteristic values characterizing the graphical representation of the physical element. In some implementations, the method includes compositing an affordance in association with the graphical representation of the physical element. In some implementations, the affordance allows interaction with the graphical representation of the physical element in accordance with the perceptual characteristic values included in the PPV.

    Generating a Semantic Construction of a Physical Setting

    公开(公告)号:US20210407185A1

    公开(公告)日:2021-12-30

    申请号:US17475004

    申请日:2021-09-14

    Applicant: Apple Inc.

    Abstract: In some implementations, a method includes obtaining environmental data corresponding to a physical environment. In some implementations, the method includes determining, based on the environmental data, a bounding surface of the physical environment. In some implementations, the method includes detecting a physical element located within the physical environment based on the environmental data. In some implementations, the method includes determining a semantic label for the physical element based on at least a portion of the environmental data corresponding to the physical element. In some implementations, the method includes generating a semantic construction of the physical environment based on the environmental data. In some implementations, the semantic construction of the physical environment includes a representation of the bounding surface, a representation of the physical element and the semantic label for the physical element.

    Generating Content Based on State Information

    公开(公告)号:US20210398360A1

    公开(公告)日:2021-12-23

    申请号:US17465342

    申请日:2021-09-02

    Applicant: Apple Inc.

    Abstract: A method includes determining a first portion of state information that is accessible to a first agent instantiated in an environment. The method includes determining a second portion of the state information that is accessible to a second agent instantiated in the environment. The method includes generating a first set of actions for a representation of the first agent based on the first portion of the state information to satisfy a first objective of the first agent. The method includes generating a second set of actions for a representation of the second agent based on the second portion of the state information to satisfy a second objective of the second agent. The method includes modifying the representations of the first and second agents based on the first and second set of actions.

    MODEL WITH MULTIPLE CONCURRENT TIMESCALES

    公开(公告)号:US20210201108A1

    公开(公告)日:2021-07-01

    申请号:US17203374

    申请日:2021-03-16

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating an environment state is performed by a device including one or more processors and non-transitory memory. The method includes obtaining a first environment state of an environment, wherein the first environment state indicates the inclusion in the environment of a first asset associated with a first timescale value and a second asset associated with a second timescale value, wherein the first environment state further indicates that the first asset has a first state of the first asset and the second asset has a first state of the second asset. The method includes determining a second state of the first asset and the second asset based on the first and second timescale value. The method includes determining a second environment state that indicates that the first asset has the second state and the second asset has the second state.

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