Patents by Inventor Patryk Laurent
Patryk Laurent 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: 20220212342Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: ApplicationFiled: January 13, 2022Publication date: July 7, 2022Inventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
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Patent number: 11224971Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: GrantFiled: June 20, 2019Date of Patent: January 18, 2022Assignee: Brain CorporationInventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
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Patent number: 10843338Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.Type: GrantFiled: May 3, 2019Date of Patent: November 24, 2020Assignee: Brain CorporationInventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
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Patent number: 10818016Abstract: Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.Type: GrantFiled: March 19, 2019Date of Patent: October 27, 2020Assignee: Brain CorporationInventors: Filip Piekniewski, Micah Richert, Dimitry Fisher, Patryk Laurent, Csaba Petre
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Patent number: 10717191Abstract: Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.Type: GrantFiled: December 18, 2017Date of Patent: July 21, 2020Assignee: Brain CorporationInventors: Filip Ponulak, Moslem Kazemi, Patryk Laurent, Oleg Sinyavskiy, Eugene Izhikevich
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Publication number: 20190366538Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: ApplicationFiled: June 20, 2019Publication date: December 5, 2019Inventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
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Publication number: 20190321973Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.Type: ApplicationFiled: May 3, 2019Publication date: October 24, 2019Inventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
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Publication number: 20190302714Abstract: Gesture recognition systems that are configured to provide users with simplified operation of various controllable devices such as, for example, in-home controllable devices. In one implementation, the gesture recognition system automatically configures itself in order to determine the respective physical locations and/or identities of controllable devices as well as an operating mode for controlling the controllable devices through predetermined gesturing. In some implementations, the gesture recognition systems are also configured to assign boundary areas associated with the controllable devices. Apparatus and methods associated with the gesture recognition systems are also disclosed.Type: ApplicationFiled: April 5, 2019Publication date: October 3, 2019Inventors: Patryk Laurent, Eugene Izhikevich
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Publication number: 20190244365Abstract: Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.Type: ApplicationFiled: March 19, 2019Publication date: August 8, 2019Inventors: Filip Piekniewski, Micah Richert, Dimitry Fisher, Patryk Laurent, Csaba Petre
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Patent number: 10369694Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: GrantFiled: April 23, 2018Date of Patent: August 6, 2019Assignee: Brain CorporationInventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
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Patent number: 10322507Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.Type: GrantFiled: October 16, 2017Date of Patent: June 18, 2019Assignee: Brain CorporationInventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
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Patent number: 10295972Abstract: Gesture recognition systems that are configured to provide users with simplified operation of various controllable devices such as, for example, in-home controllable devices. In one implementation, the gesture recognition system automatically configures itself in order to determine the respective physical locations and/or identities of controllable devices as well as an operating mode for controlling the controllable devices through predetermined gesturing. In some implementations, the gesture recognition systems are also configured to assign boundary areas associated with the controllable devices. Apparatus and methods associated with the gesture recognition systems are also disclosed.Type: GrantFiled: April 29, 2016Date of Patent: May 21, 2019Assignee: Brain CorporationInventors: Patryk Laurent, Eugene Izhikevich
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Patent number: 10282849Abstract: Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.Type: GrantFiled: June 19, 2017Date of Patent: May 7, 2019Assignee: Brain CorporationInventors: Filip Piekniewski, Micah Richert, Dimitry Fisher, Patryk Laurent, Csaba Petre
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Publication number: 20180319015Abstract: An apparatus and methods for training and/or operating a robotic device to perform a composite task comprising a plurality of subtasks. Subtasks may be arranged in a hierarchy. Individual tasks of the hierarchy may be operated by a respective learning controller. Individual learning controllers may interface to appropriate components of feature extractor configured to detect features in sensory input. Individual learning controllers may be trained to produce activation output based on occurrence of one or more relevant features and using training input. Output of a higher level controller may be provided as activation indication to one or more lower level controllers. Inactive activation indication may be utilized to deactivate one or more components thereby improving operational efficiency. Output of a given feature extractor may be shared between two or more learning controllers.Type: ApplicationFiled: July 10, 2018Publication date: November 8, 2018Inventors: Oleg Sinyavskiy, Jean-Baptiste Passot, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich
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Publication number: 20180311817Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: ApplicationFiled: April 23, 2018Publication date: November 1, 2018Inventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja lbarz Gabardos, Eugene lzhikevich
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Publication number: 20180272529Abstract: Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.Type: ApplicationFiled: December 18, 2017Publication date: September 27, 2018Inventors: Filip Ponulak, Moslem Kazemi, Patryk Laurent, Oleg Sinyavskiy, Eugene Izhikevich
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Publication number: 20180260685Abstract: A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. The remote controller may comprise multiple control elements configured to activate respective portions of the robot platform. Based on training, the remote controller may configure composite controls configured based two or more of control elements. Activation of a composite control may enable the robot to perform a task.Type: ApplicationFiled: October 16, 2017Publication date: September 13, 2018Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja lbarz Gabardos, Eugene lzhikevich, Vadim Polonichko
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Patent number: 9950426Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.Type: GrantFiled: April 18, 2016Date of Patent: April 24, 2018Assignee: Brain CorporationInventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
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Publication number: 20180099409Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.Type: ApplicationFiled: October 16, 2017Publication date: April 12, 2018Inventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
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Publication number: 20180018775Abstract: Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.Type: ApplicationFiled: June 19, 2017Publication date: January 18, 2018Inventors: Filip Piekniewski, Micah Richert, Dimitry Fisher, Patryk Laurent, Csaba Petre