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公开(公告)号：US10981270B1
公开(公告)日：2021-04-20
申请号：US16530711
申请日：2019-08-02
Applicant: X Development LLC
Inventor： Peter Pastor Sampedro , Mrinal Kalakrishnan , Ali Yahya Valdovinos , Adrian Li , Kurt Konolige , Vincent Dureau
Abstract: Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.
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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：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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公开(公告)号：US11571809B1
公开(公告)日：2023-02-07
申请号：US17017920
申请日：2020-09-11
Applicant: X Development LLC
Inventor： Cristian Bodnar , Adrian Li , Karol Hausman , Peter Pastor Sampedro , Mrinal Kalakrishnan
Abstract: Techniques are described herein for robotic control using value distributions. In various implementations, as part of performing a robotic task, state data associated with the robot in an environment may be generated based at least in part on vision data captured by a vision component of the robot. A plurality of candidate actions may be sampled, e.g., from continuous action space. A trained critic neural network model that represents a learned value function may be used to process a plurality of state-action pairs to generate a corresponding plurality of value distributions. Each state-action pair may include the state data and one of the plurality of sampled candidate actions. The state-action pair corresponding to the value distribution that satisfies one or more criteria may be selected from the plurality of state-action pairs. The robot may then be controlled to implement the sampled candidate action of the selected state-action pair.
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公开(公告)号：US10748057B1
公开(公告)日：2020-08-18
申请号：US15272112
申请日：2016-09-21
Applicant: X Development LLC
Inventor： Adrian Li , Mrinal Kalakrishnan
Abstract: Methods, apparatus, and computer readable media related to combining and/or training one or more neural network modules based on version identifier(s) assigned to the neural network module(s). Some implementations are directed to using version identifiers of neural network modules in determining whether and/or how to combine multiple neural network modules to generate a combined neural network model for use by a robot and/or other apparatus. Some implementations are additionally or alternatively directed to assigning a version identifier to an endpoint of a neural network module based on one or more other neural network modules to which the neural network module is joined during training of the neural network module.
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Patent Agency Ranking
公开(公告)号：US11610153B1
公开(公告)日：2023-03-21
申请号：US16729712
申请日：2019-12-30
Applicant: X Development LLC
Inventor： Alexander Herzog , Adrian Li , Mrinal Kalakrishnan , Benjamin Holson
Abstract: Utilizing at least one existing policy (e.g. a manually engineered policy) for a robotic task, in generating reinforcement learning (RL) data that can be used in training an RL policy for an instance of RL of the robotic task. The existing policy can be one that, standing alone, will not generate data that is compatible with the instance of RL for the robotic task. In contrast, the generated RL data is compatible with RL for the robotic task at least by virtue of it including state data that is in a state space of the RL for the robotic task, and including actions that are in the action space of the RL for the robotic task. The generated RL data can be used in at least some of the initial training for the RL policy using reinforcement learning.
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公开(公告)号：US20220245503A1
公开(公告)日：2022-08-04
申请号：US17161845
申请日：2021-01-29
Applicant: X Development LLC
Inventor： Adrian Li , Benjamin Holson , Alexander Herzog , Mrinal Kalakrishnan
Abstract: Implementations disclosed herein relate to utilizing at least one existing manually engineered policy, for a robotic task, in training an RL policy model that can be used to at least selectively replace a portion of the engineered policy. The RL policy model can be trained for replacing a portion of a robotic task and can be trained based on data from episodes of attempting performance of the robotic task, including episodes in which the portion is performed based on the engineered policy and/or other portion(s) are performed based on the engineered policy. Once trained, the RL policy model can be used, at least selectively and in lieu of utilization of the engineered policy, to perform the portion of robotic task, while other portion(s) of the robotic task are performed utilizing the engineered policy and/or other similarly trained (but distinct) RL policy model(s).
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公开(公告)号：US11325252B2
公开(公告)日：2022-05-10
申请号：US16570522
申请日：2019-09-13
Applicant: X Development LLC
Inventor： Adrian Li , Peter Pastor Sampedro , Mengyuan Yan , Mrinal Kalakrishnan
Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.
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公开(公告)号：US20200086483A1
公开(公告)日：2020-03-19
申请号：US16570522
申请日：2019-09-13
Applicant: X Development LLC
Inventor： Adrian Li , Peter Pastor Sampedro , Mengyuan Yan , Mrinal Kalakrishnan
IPC: B25J9/16
Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.
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公开(公告)号：US11615291B1
公开(公告)日：2023-03-28
申请号：US16918181
申请日：2020-07-01
Applicant: X Development LLC
Inventor： Adrian Li , Mrinal Kalakrishnan
Abstract: Methods, apparatus, and computer readable media related to combining and/or training one or more neural network modules based on version identifier(s) assigned to the neural network module(s). Some implementations are directed to using version identifiers of neural network modules in determining whether and/or how to combine multiple neural network modules to generate a combined neural network model for use by a robot and/or other apparatus. Some implementations are additionally or alternatively directed to assigning a version identifier to an endpoint of a neural network module based on one or more other neural network modules to which the neural network module is joined during training of the neural network module.
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