Patents by Inventor Jean-Baptiste Passot
Jean-Baptiste Passot 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: 20180243903Abstract: Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.Type: ApplicationFiled: April 30, 2018Publication date: August 30, 2018Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Eugene Izhikevich
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Publication number: 20180215039Abstract: Systems and methods assisting a robotic apparatus are disclosed. In some exemplary implementations, a robot can encounter situations where the robot cannot proceed and/or does not know with a high degree of certainty it can proceed. Accordingly, the robot can determine that it has encountered an error and/or assist event. In some exemplary implementations, the robot can receive assistance from an operator and/or attempt to resolve the issue itself. In some cases, the robot can be configured to delay actions in order to allow resolution of the error and/or assist event.Type: ApplicationFiled: February 2, 2017Publication date: August 2, 2018Inventors: Oleg Sinyavskiy, Jean-Baptiste Passot, Borja Ibarz Gabardos, Diana Vu Le
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Patent number: 10016896Abstract: Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.Type: GrantFiled: June 30, 2016Date of Patent: July 10, 2018Assignee: BRAIN CORPORATIONInventors: Oleg Sinyavskiy, Borja Ibarz Gabardos, Jean-Baptiste Passot
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Patent number: 10001780Abstract: Systems and methods for dynamic route planning in autonomous navigation are disclosed. In some exemplary implementations, a robot can have one or more sensors configured to collect data about an environment including detected points on one or more objects in the environment. The robot can then plan a route in the environment, where the route can comprise one or more route poses. The route poses can include a footprint indicative at least in part of a pose, size, and shape of the robot along the route. Each route pose can have a plurality of points therein. Based on forces exerted on the points of each route pose by other route poses, objects in the environment, and others, each route pose can reposition. Based at least in part on interpolation performed on the route poses (some of which may be repositioned), the robot can dynamically route.Type: GrantFiled: November 2, 2016Date of Patent: June 19, 2018Assignee: Brain CorporationInventors: Borja Ibarz Gabardos, Jean-Baptiste Passot
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Patent number: 9987743Abstract: Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.Type: GrantFiled: March 13, 2014Date of Patent: June 5, 2018Assignee: BRAIN CORPORATIONInventors: Eugene Izhikevich, Dimitry Fisher, Jean-Baptiste Passot
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Publication number: 20180126550Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.Type: ApplicationFiled: September 18, 2017Publication date: May 10, 2018Inventors: Eugene Izhikevich, Oleg Sinyavskiy, Jean-Baptiste Passot
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Publication number: 20180120116Abstract: Systems and methods for robotic mapping are disclosed. In some exemplary implementations, a robot can travel in an environment. From travelling in the environment, the robot can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by a sensor of the robot at a location in the environment. In some exemplary implementations, the robot can generate a map of the environment from the graph. In some cases, to facilitate map generation, the robot can constrain the graph to start and end at a substantially similar location. The robot can also perform scan matching on extended scan groups, determined from identifying overlap between scans, to further determine the location of features in a map.Type: ApplicationFiled: November 1, 2016Publication date: May 3, 2018Inventors: Jaldert Rombouts, Borja Ibarz Gabardos, Jean-Baptiste Passot, Andrew Smith
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Publication number: 20180120856Abstract: Systems and methods for dynamic route planning in autonomous navigation are disclosed. In some exemplary implementations, a robot can have one or more sensors configured to collect data about an environment including detected points on one or more objects in the environment. The robot can then plan a route in the environment, where the route can comprise one or more route poses. The route poses can include a footprint indicative at least in part of a pose, size, and shape of the robot along the route. Each route pose can have a plurality of points therein. Based on forces exerted on the points of each route pose by other route poses, objects in the environment, and others, each route poses can reposition. Based at least in part on interpolation performed on the route poses (some of which may be repositioned), the robot can dynamically route.Type: ApplicationFiled: November 2, 2016Publication date: May 3, 2018Inventors: Borja Ibarz Gabardos, Jean-Baptiste Passot
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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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Patent number: 9862092Abstract: Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.Type: GrantFiled: December 3, 2015Date of Patent: January 9, 2018Assignee: Brain CorporationInventors: Eugene Izhikevich, Dimitry Fisher, Jean-Baptiste Passot, Heathcliff Hatcher, Vadim Polonichko
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Publication number: 20180001474Abstract: Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.Type: ApplicationFiled: June 30, 2016Publication date: January 4, 2018Inventors: Oleg Sinyavskiy, Borja Ibarz Gabardos, Jean-Baptiste Passot
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Patent number: 9821457Abstract: Apparatus and methods for training of robotic devices. 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. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.Type: GrantFiled: January 12, 2015Date of Patent: November 21, 2017Assignee: Brain CorporationInventors: Patryk Laurent, Jean-Baptiste Passot, Mark Wildie, Eugene M. Izhikevich, Vadim Polonichko
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Publication number: 20170329347Abstract: Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.Type: ApplicationFiled: May 11, 2016Publication date: November 16, 2017Inventors: Jean-Baptiste Passot, Andrew Smith, Botond Szatmary, Borja Ibarz Gabardos, Cody Griffin, Jaldert Rombouts, Oleg Sinyavskiy, Eugene Izhikevich
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Publication number: 20170329333Abstract: Systems and methods for initializing a robot to autonomously travel a route are disclosed. In some exemplary implementations, a robot can detect an initialization object and then determine its position relative to that initialization object. The robot can then learn a route by user demonstration, where the robot associates actions along that route with positions relative to the initialization object. The robot can later detect the initialization object again and determine its position relative to that initialization object. The robot can then autonomously navigate the learned route, performing actions associated with positions relative to the initialization object.Type: ApplicationFiled: May 11, 2016Publication date: November 16, 2017Inventors: Jean-Baptiste Passot, Jaldert Rombouts, Cody Griffin, John Black
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Patent number: 9789605Abstract: 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: June 6, 2016Date of Patent: October 17, 2017Assignee: 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: 9792546Abstract: 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: GrantFiled: June 14, 2013Date of Patent: October 17, 2017Assignee: Brain CorporationInventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich, Vadim Polonichko
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Publication number: 20170266805Abstract: Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.Type: ApplicationFiled: March 30, 2017Publication date: September 21, 2017Inventors: Eugene Izhikevich, Dimitry Fisher, Jean-Baptiste Passot
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Patent number: 9764468Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.Type: GrantFiled: March 15, 2013Date of Patent: September 19, 2017Assignee: Brain CorporationInventors: Eugene Izhikevich, Oleg Sinyavskiy, Jean-Baptiste Passot
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Publication number: 20170203437Abstract: Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.Type: ApplicationFiled: February 13, 2017Publication date: July 20, 2017Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Eugene Izhikevich