Patents by Inventor Soeren Pirk
Soeren Pirk has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240094736Abstract: Training and/or utilizing a high-level neural network (NN) model, such as a sequential NN model. The high-level NN model, when trained, can be used to process a sequence of consecutive state data instances (e.g., N most recent, including a current state date instance) to generate a sequence of outputs that indicate a sequence of position deltas. The sequence of position deltas can be used to generate an intermediate target position for navigation and, optionally, an intermediate target orientation that corresponds to the intermediate target position. The intermediate target position and, optionally, the intermediate target orientation, can be provided to a low-level navigation policy, such as an MPC policy, and used by the low-level navigation policy as its goal position (and optionally goal orientation) for a plurality of iterations (e.g., until a new intermediate target position (and optionally new target orientation) is generated using the high-level NN model.Type: ApplicationFiled: August 30, 2023Publication date: March 21, 2024Inventors: Catie Cuan, Tsang-Wei Lee, Anthony G. Francis, JR., Alexander Toshev, Soeren Pirk
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Patent number: 11887363Abstract: Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.Type: GrantFiled: September 27, 2019Date of Patent: January 30, 2024Assignee: GOOGLE LLCInventors: Soeren Pirk, Yunfei Bai, Pierre Sermanet, Seyed Mohammad Khansari Zadeh, Harrison Lynch
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Publication number: 20230419521Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.Type: ApplicationFiled: September 13, 2023Publication date: December 28, 2023Inventors: Vincent Michael Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
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Publication number: 20230334842Abstract: Methods, systems, and apparatus for processing inputs that include video frames using neural networks. In one aspect, a system comprises one or more computers configured to obtain a set of one or more training images and, for each training image, ground truth instance data that identifies, for each of one or more object instances, a corresponding region of the training image that depicts the object instance. For each training image in the set, the one or more computers process the training image using an instance segmentation neural network to generate an embedding output comprising a respective embedding for each of a plurality of output pixels. The one or more computers then train the instance segmentation neural network to minimize a loss function.Type: ApplicationFiled: April 18, 2023Publication date: October 19, 2023Inventors: Alex Zihao Zhu, Vincent Michael Casser, Henrik Kretzschmar, Reza Mahjourian, Soeren Pirk
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Patent number: 11783500Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.Type: GrantFiled: September 5, 2019Date of Patent: October 10, 2023Assignee: Google LLCInventors: Vincent Michael Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
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Patent number: 11544498Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.Type: GrantFiled: March 5, 2021Date of Patent: January 3, 2023Assignee: Google LLCInventors: Ariel Gordon, Soeren Pirk, Anelia Angelova, Vincent Michael Casser, Yao Lu, Anthony Brohan, Zhao Chen, Jan Dlabal
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Publication number: 20220331962Abstract: Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.Type: ApplicationFiled: September 9, 2020Publication date: October 20, 2022Inventors: Soeren Pirk, Seyed Mohammad Khansari Zadeh, Karol Hausman, Alexander Toshev
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Publication number: 20210390407Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a perspective computer vision model. The model is configured to receive input data characterizing an input scene in an environment from an input viewpoint and to process the input data in accordance with a set of model parameters to generate an output perspective representation of the scene from the input viewpoint. The system trains the model based on first data characterizing a scene in the environment from a first viewpoint and second data characterizing the scene in the environment from a second, different viewpoint.Type: ApplicationFiled: June 10, 2021Publication date: December 16, 2021Inventors: Vincent Michael Casser, Yuning Chai, Dragomir Anguelov, Hang Zhao, Henrik Kretzschmar, Reza Mahjourian, Anelia Angelova, Ariel Gordon, Soeren Pirk
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Publication number: 20210334599Abstract: Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.Type: ApplicationFiled: September 27, 2019Publication date: October 28, 2021Inventors: Soeren Pirk, Yunfei Bai, Pierre Sermanet, Seyed Mohammad Khansari Zadeh, Harrison Lynch
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Publication number: 20210319578Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.Type: ApplicationFiled: September 5, 2019Publication date: October 14, 2021Inventors: Vincent Michael Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
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Publication number: 20210279511Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.Type: ApplicationFiled: March 5, 2021Publication date: September 9, 2021Inventors: Ariel Gordon, Soeren Pirk, Anelia Angelova, Vincent Michael Casser, Yao Lu, Anthony Brohan, Zhao Chen, Jan Dlabal
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Patent number: 11100646Abstract: A method for generating a predicted segmentation map for potential objects in a future scene depicted in a future image is described. The method includes receiving input images that depict a same scene; processing a current input image to generate a segmentation map for potential objects in the current input image and a respective depth map; generating a point cloud for the current input image; processing the input images to generate, for each pair of two input images in the sequence, a respective ego-motion output that characterizes motion of the camera between the two input images; processing the ego-motion outputs to generate a future ego-motion output; processing the point cloud of the current input image and the future ego-motion output to generate a future point cloud; and processing the future point cloud to generate the predicted segmentation map for potential objects in the future scene depicted in the future image.Type: GrantFiled: September 6, 2019Date of Patent: August 24, 2021Assignee: Google LLCInventors: Suhani Vora, Reza Mahjourian, Soeren Pirk, Anelia Angelova
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Publication number: 20210101286Abstract: Implementations relate to training a point cloud prediction model that can be utilized to process a single-view two-and-a-half-dimensional (2.5D) observation of an object, to generate a domain-invariant three-dimensional (3D) representation of the object. Implementations additionally or alternatively relate to utilizing the domain-invariant 3D representation to train a robotic manipulation policy model using, as at least part of the input to the robotic manipulation policy model during training, the domain-invariant 3D representations of simulated objects to be manipulated. Implementations additionally or alternatively relate to utilizing the trained robotic manipulation policy model in control of a robot based on output generated by processing generated domain-invariant 3D representations utilizing the robotic manipulation policy model.Type: ApplicationFiled: February 28, 2020Publication date: April 8, 2021Inventors: Honglak Lee, Xinchen Yan, Soeren Pirk, Yunfei Bai, Seyed Mohammad Khansari Zadeh, Yuanzheng Gong, Jasmine Hsu
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Publication number: 20210073997Abstract: This disclosure describes a system including one or more computers and one or more non-transitory storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations for generating a predicted segmentation map for potential objects in a future scene depicted in a future image.Type: ApplicationFiled: September 6, 2019Publication date: March 11, 2021Inventors: Suhani Vora, Reza Mahjourian, Soeren Pirk, Anelia Angelova