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公开(公告)号：US11625852B1
公开(公告)日：2023-04-11
申请号：US17114083
申请日：2020-12-07
Applicant: X Development LLC
Inventor： Mrinal Kalakrishnan , Adrian Ling Hin Li , Nicolas Hudson
IPC: G06T7/73 , G06T7/60 , G06T7/11 , G06V10/42 , G06V30/194
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for predicting object pose. In one aspect, a method includes receiving an image of an object having one or more feature points; providing the image as an input to a neural network subsystem trained to receive images of objects and to generate an output including a heat map for each feature point; applying a differentiable transformation on each heat map to generate respective one or more feature coordinates for each feature point; providing the feature coordinates for each feature point as input to an object pose solver configured to compute a predicted object pose for the object, wherein the predicted object pose for the object specifies a position and an orientation of an object; and receiving, at the output of the object pose solver, a predicted object pose for the object in the image.
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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：US11314987B2
公开(公告)日：2022-04-26
申请号：US16692509
申请日：2019-11-22
Applicant: X Development LLC
Inventor： Paul Wohlhart , Stephen James , Mrinal Kalakrishnan , Konstantinos Bousmalis
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.
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公开(公告)号：US10773382B2
公开(公告)日：2020-09-15
申请号：US15913212
申请日：2018-03-06
Applicant: X Development LLC
Inventor： Yunfei Bai , Kuan Fang , Stefan Hinterstoisser , Mrinal Kalakrishnan
IPC: B25J9/16
Abstract: Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.
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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：US11607807B2
公开(公告)日：2023-03-21
申请号：US17230628
申请日：2021-04-14
Applicant: X Development LLC
Inventor： Seyed Mohammad Khansari Zadeh , Mrinal Kalakrishnan , Paul Wohlhart
Abstract: Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.
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公开(公告)号：US20220215208A1
公开(公告)日：2022-07-07
申请号：US17656137
申请日：2022-03-23
Applicant: X Development LLC
Inventor： Paul Wohlhart , Stephen James , Mrinal Kalakrishnan , Konstantinos Bousmalis
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.
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公开(公告)号：US11007642B2
公开(公告)日：2021-05-18
申请号：US16167596
申请日：2018-10-23
Applicant: X Development LLC
Inventor： Seyed Mohammad Khansari Zadeh , Mrinal Kalakrishnan , Paul Wohlhart
Abstract: Training and/or use of a machine learning model for placement of an object secured by an end effector of a robot. A trained machine learning model can be used to process: (1) a current image, captured by a vision component of a robot, that captures an end effector securing an object; (2) a candidate end effector action that defines a candidate motion of the end effector; and (3) a target placement input that indicates a target placement location for the object. Based on the processing, a prediction can be generated that indicates likelihood of successful placement of the object in the target placement location with application of the motion defined by the candidate end effector action. At many iterations, the candidate end effector action with the highest probability is selected and control commands provided to cause the end effector to move in conformance with the corresponding end effector action. When at least one release criteria is satisfied, control commands can be provided to cause the end effector to release the object, thereby leading to the object being placed in the target placement location.
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公开(公告)号：US10960539B1
公开(公告)日：2021-03-30
申请号：US15705655
申请日：2017-09-15
Applicant: X Development LLC
Inventor： Mrinal Kalakrishnan , Ali Hamid Yahya Valdovinos , Adrian Ling Hin Li , Yevgen Chebotar , Sergey Vladimir Levine
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing a plurality of instances of the robotic task. For each instance of the robotic task, the method includes generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task in accordance with current values of the parameters of the global policy neural network, and optimizing a local policy controller that is specific to the instance on the trajectory of state-action pairs for the instance. The method further includes generating training data for the global policy neural network using the local policy controllers, and training the global policy neural network on the training data to adjust the current values of the parameters of the global policy neural network.
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20.发明申请公开(公告)号：US20190084151A1
公开(公告)日：2019-03-21
申请号：US15913212
申请日：2018-03-06
Applicant: X Development LLC
Inventor： Yunfei Bai , Kuan Fang , Stefan Hinterstoisser , Mrinal Kalakrishnan
IPC: B25J9/16
Abstract: Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.
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