Patents by Inventor Borja Ibarz Gabardos
Borja Ibarz Gabardos 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).
-
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
-
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
-
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
-
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
-
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
-
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
-
Publication number: 20170291301Abstract: A robotic device may be operated by a learning controller comprising a feature learning configured to determine control signal based on sensory input. An input may be analyzed in order to determine occurrence of one or more features. Features in the input may be associated with the control signal during online supervised training. During training, learning process may be adapted based on training input and the predicted output. A combination of the predicted and the target output may be provided to a robotic device to execute a task. Feature determination may comprise online adaptation of input, sparse encoding transformations. Computations related to learning process adaptation and feature detection may be performed on board by the robotic device in real time thereby enabling autonomous navigation by trained robots.Type: ApplicationFiled: April 24, 2017Publication date: October 12, 2017Inventors: Borja Ibarz Gabardos, Andrew Smith, Peter O'Connor
-
Patent number: 9630318Abstract: A robotic device may be operated by a learning controller comprising a feature learning configured to determine control signal based on sensory input. An input may be analyzed in order to determine occurrence of one or more features. Features in the input may be associated with the control signal during online supervised training. During training, learning process may be adapted based on training input and the predicted output. A combination of the predicted and the target output may be provided to a robotic device to execute a task. Feature determination may comprise online adaptation of input, sparse encoding transformations. Computations related to learning process adaptation and feature detection may be performed on board by the robotic device in real time thereby enabling autonomous navigation by trained robots.Type: GrantFiled: November 14, 2014Date of Patent: April 25, 2017Assignee: Brain CorporationInventors: Borja Ibarz Gabardos, Andrew Smith, Peter O'Connor
-
Publication number: 20170001309Abstract: Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task.Type: ApplicationFiled: July 1, 2016Publication date: January 5, 2017Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich
-
Publication number: 20160303738Abstract: 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 18, 2016Publication date: October 20, 2016Inventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
-
Publication number: 20160279790Abstract: 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: June 6, 2016Publication date: September 29, 2016Inventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
-
Patent number: 9384443Abstract: Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task. Upon learning, user's knowledge may be transferred to the robot's controller to enable task execution in absence of subsequent inputs from the user.Type: GrantFiled: June 14, 2013Date of Patent: July 5, 2016Assignee: BRAIN CORPORATIONInventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich
-
Patent number: 9358685Abstract: 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: February 3, 2014Date of Patent: June 7, 2016Assignee: BRAIN CORPORATIONInventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
-
Patent number: 9314924Abstract: 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 14, 2013Date of Patent: April 19, 2016Assignee: Brain CorporationInventors: Patryk Laurent, Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Borja Ibarz Gabardos, Eugene Izhikevich
-
Publication number: 20160096270Abstract: A robotic device may be operated by a learning controller comprising a feature learning configured to determine control signal based on sensory input. An input may be analyzed in order to determine occurrence of one or more features. Features in the input may be associated with the control signal during online supervised training. During training, learning process may be adapted based on training input and the predicted output. A combination of the predicted and the target output may be provided to a robotic device to execute a task. Feature determination may comprise online adaptation of input, sparse encoding transformations. Computations related to learning process adaptation and feature detection may be performed on board by the robotic device in real time thereby enabling autonomous navigation by trained robots.Type: ApplicationFiled: November 14, 2014Publication date: April 7, 2016Inventors: Borja Ibarz Gabardos, Andrew Smith, Peter O'Connor
-
Patent number: 9152915Abstract: Optical flow for a moving platform may be encoded into pulse output. Optical flow contribution induced due to the platform self-motion may be cancelled. The cancellation may be effectuated by (i) encoding the platform motion into pulse output; and (ii) inhibiting pulse generation by neurons configured to encode optical flow component optical flow that occur based on self-motion. The motion encoded may be coupled to the optical flow encoder via one or more connections. Connection propagation delay may be configured during encoder calibration in the absence of obstacles so as to provide system specific delay matrix. The inhibition may be based on a coincident arrival of the motion spiking signal via the calibrated connections to the optical flow encoder neurons. The coincident motion pulse arrival may be utilized in order to implement an addition of two or more vector properties.Type: GrantFiled: November 29, 2012Date of Patent: October 6, 2015Assignee: Brain CorporationInventors: Borja Ibarz Gabardos, Eugene Izhikevich
-
Publication number: 20150217449Abstract: 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: February 3, 2014Publication date: August 6, 2015Applicant: Brain CorporationInventors: Philip Meier, Jean-Baptiste Passot, Borja Ibarz Gabardos, Patryk Laurent, Oleg Sinyavskiy, Peter O'Connor, Eugene Izhikevich
-
Publication number: 20140371907Abstract: Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task.Type: ApplicationFiled: June 14, 2013Publication date: December 18, 2014Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich
-
Publication number: 20140371912Abstract: 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: June 14, 2013Publication date: December 18, 2014Inventors: Jean-Baptiste Passot, Oleg Sinyavskiy, Filip Ponulak, Patryk Laurent, Borja Ibarz Gabardos, Eugene Izhikevich, Vadim Polonichko