Patents by Inventor Yusuf Aytar
Yusuf Aytar 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: 20240042600Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.Type: ApplicationFiled: June 8, 2023Publication date: February 8, 2024Inventors: Serkan Cabi, Ziyu Wang, Alexander Novikov, Ksenia Konyushkova, Sergio Gomez Colmenarejo, Scott Ellison Reed, Misha Man Ray Denil, Jonathan Karl Scholz, Oleg O. Sushkov, Rae Chan Jeong, David Barker, David Budden, Mel Vecerik, Yusuf Aytar, Joao Ferdinando Gomes de Freitas
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Publication number: 20230330846Abstract: It is described a system implemented as computer programs on one or more computers in one or more locations that trains a policy neural network that is used to control a robot, i.e., to select actions to be performed by the robot while the robot is interacting with an environment, through imitation learning in order to cause the robot to perform particular tasks in the environment.Type: ApplicationFiled: October 1, 2021Publication date: October 19, 2023Inventors: Yuxiang Zhou, Yusuf Aytar, Konstantinos Bousmalis
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Publication number: 20230274548Abstract: Techniques are disclosed that enable processing a video capturing a periodic activity using a repetition network to generate periodic output (e.g., a period length of the periodic activity captured in the video and/or a frame wise periodicity indication of the video capturing the periodic activity). Various implementations include a class agnostic repetition network which can be used to generate periodic output for a wide variety of periodic activities. Additional or alternative implementations include generating synthetic repetition videos which can be utilized to train the repetition network.Type: ApplicationFiled: June 10, 2020Publication date: August 31, 2023Inventors: Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Andrew Zisserman, Pierre Sermanet
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Patent number: 11712799Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.Type: GrantFiled: September 14, 2020Date of Patent: August 1, 2023Assignee: DeepMind Technologies LimitedInventors: Serkan Cabi, Ziyu Wang, Alexander Novikov, Ksenia Konyushkova, Sergio Gomez Colmenarejo, Scott Ellison Reed, Misha Man Ray Denil, Jonathan Karl Scholz, Oleg O. Sushkov, Rae Chan Jeong, David Barker, David Budden, Mel Vecerik, Yusuf Aytar, Joao Ferdinando Gomes de Freitas
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Patent number: 11663441Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.Type: GrantFiled: September 27, 2019Date of Patent: May 30, 2023Assignee: DeepMind Technologies LimitedInventors: Scott Ellison Reed, Yusuf Aytar, Ziyu Wang, Tom Paine, Sergio Gomez Colmenarejo, David Budden, Tobias Pfaff, Aaron Gerard Antonius van den Oord, Oriol Vinyals, Alexander Novikov
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Publication number: 20220004883Abstract: An encoder neural network is described which can encode a data item, such as a frame of a video, to form a respective encoded data item. Data items of a first data sequence are associated with respective data items of a second sequence, by determining which of the encoded data items of the second sequence is closest to the encoded data item produced from each data item of the first sequence. Thus, the two data sequences are aligned. The encoder neural network is trained automatically using a training set of data sequences, by an iterative process of successively increasing cycle consistency between pairs of the data sequences.Type: ApplicationFiled: November 21, 2019Publication date: January 6, 2022Inventors: Yusuf Aytar, Debidatta Dwibedi, Andrew Zisserman, Jonathan Tompson, Pierre Sermanet
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Publication number: 20210103815Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network for use in controlling a real-world agent in a real-world environment. One of the methods includes training the policy neural network by optimizing a first task-specific objective that measures a performance of the policy neural network in controlling a simulated version of the real-world agent; and then training the policy neural network by jointly optimizing (i) a self-supervised objective that measures at least a performance of internal representations generated by the policy neural network on a self-supervised task performed on real-world data and (ii) a second task-specific objective that measures the performance of the policy neural network in controlling the simulated version of the real-world agent.Type: ApplicationFiled: October 7, 2020Publication date: April 8, 2021Inventors: Rae Chan Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jacqueline Ok-chan Kay, Thomas Lampe, Konstantinos Bousmalis, Francesco Nori
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Publication number: 20210078169Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.Type: ApplicationFiled: September 14, 2020Publication date: March 18, 2021Inventors: Serkan Cabi, Ziyu Wang, Alexander Novikov, Ksenia Konyushkova, Sergio Gomez Colmenarejo, Scott Ellison Reed, Misha Man Ray Denil, Jonathan Karl Scholz, Oleg O. Sushkov, Rae Chan Jeong, David Barker, David Budden, Mel Vecerik, Yusuf Aytar, Joao Ferdinando Gomes de Freitas
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Publication number: 20200104680Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.Type: ApplicationFiled: September 27, 2019Publication date: April 2, 2020Inventors: Scott Ellison Reed, Yusuf Aytar, Ziyu Wang, Tom Paine, Sergio Gomez Colmenarejo, David Budden, Tobias Pfaff, Aaron Gerard Antonius van den Oord, Oriol Vinyals, Alexander Novikov