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公开(公告)号：US20230021778A1
公开(公告)日：2023-01-26
申请号：US17961926
申请日：2022-10-07
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Andrew Smith , Botond Szatmary , Borja Ibarz Gabardos , Cody Griffin , Jaldert Rombouts , Oleg Sinyavskiy , Eugene Izhikevich
Abstract: 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.
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公开(公告)号：US10823576B2
公开(公告)日：2020-11-03
申请号：US16356160
申请日：2019-03-18
Applicant: BRAIN CORPORATION
Inventor： Jaldert Rombouts , Borja Ibarz Gabardos , Jean-Baptiste Passot , Andrew Smith
Abstract: 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.
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公开(公告)号：US10293485B2
公开(公告)日：2019-05-21
申请号：US15474816
申请日：2017-03-30
Applicant: BRAIN CORPORATION
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Borja Ibarz Gabardos , Diana Vu Le
Abstract: Systems and methods for robotic path planning are disclosed. In some implementations of the present disclosure, a robot can generate a cost map associated with an environment of the robot. The cost map can comprise a plurality of pixels each corresponding to a location in the environment, where each pixel can have an associated cost. The robot can further generate a plurality of masks having projected path portions for the travel of the robot within the environment, where each mask comprises a plurality of mask pixels that correspond to locations in the environment. The robot can then determine a mask cost associated with each mask based at least in part on the cost map and select a mask based at least in part on the mask cost. Based on the projected path portions within the selected mask, the robot can navigate a space.
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公开(公告)号：US20190121365A1
公开(公告)日：2019-04-25
申请号：US16168368
申请日：2018-10-23
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Andrew Smith , Botond Szatmary , Borja Ibarz Gabardos , Cody Griffin , Jaldert Rambouts , Oleg Sinyavskiy , Eugene Izhikevich
Abstract: 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.
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公开(公告)号：US20180364724A1
公开(公告)日：2018-12-20
申请号：US16011499
申请日：2018-06-18
Applicant: Brain Corporation
Inventor： Borja Ibarz Gabardos , Jean-Baptiste Passot
CPC classification number: G05D1/0214 , A47L11/4011 , A47L11/4061 , A47L2201/04 , G01C21/3415 , G01C21/343 , G05D1/0088 , G05D1/0274 , G05D1/0276 , G05D2201/0203
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20180319015A1
公开(公告)日：2018-11-08
申请号：US16031950
申请日：2018-07-10
Applicant: Brain Corporation
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Patryk Laurent , Borja Ibarz Gabardos , Eugene Izhikevich
CPC classification number: B25J9/163 , B25J9/0081 , B25J9/1607 , B25J9/161 , B25J9/1666 , B25J9/1697 , G05D1/0088 , G05D1/0246 , G05D2201/02 , G06N3/00 , G06N3/008 , G06N3/049 , G06N99/005 , Y10S901/01 , Y10S901/03 , Y10S901/09 , Y10S901/47
Abstract: 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.
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公开(公告)号：US20180281191A1
公开(公告)日：2018-10-04
申请号：US15474816
申请日：2017-03-30
Applicant: BRAIN CORPORATION
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Borja Ibarz Gabardos , Diana Vu Le
IPC: B25J9/16 , B25J11/00 , G05D1/02 , A47L11/283 , A47L11/40
CPC classification number: B25J9/1666 , A47L11/283 , A47L11/4008 , A47L11/4011 , A47L11/4061 , A47L11/4066 , B25J11/0085 , G01C21/00 , G05D1/0214 , G05D1/0217 , G05D1/0274 , G05D2201/0203
Abstract: Systems and methods for robotic path planning are disclosed. In some implementations of the present disclosure, a robot can generate a cost map associated with an environment of the robot. The cost map can comprise a plurality of pixels each corresponding to a location in the environment, where each pixel can have an associated cost. The robot can further generate a plurality of masks having projected path portions for the travel of the robot within the environment, where each mask comprises a plurality of mask pixels that correspond to locations in the environment. The robot can then determine a mask cost associated with each mask based at least in part on the cost map and select a mask based at least in part on the mask cost. Based on the projected path portions within the selected mask, the robot can navigate a space.
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公开(公告)号：US20180120856A1
公开(公告)日：2018-05-03
申请号：US15341612
申请日：2016-11-02
Applicant: BRAIN CORPORATION
Inventor： Borja Ibarz Gabardos , Jean-Baptiste Passot
CPC classification number: G05D1/0214 , A47L11/4011 , A47L11/4061 , A47L2201/04 , G01C21/3415 , G01C21/343 , G05D1/0088 , G05D1/0276 , G05D2201/0203
Abstract: 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.
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公开(公告)号：US09950426B2
公开(公告)日：2018-04-24
申请号：US15132003
申请日：2016-04-18
Applicant: BRAIN Corporation
Inventor： Patryk Laurent , Jean-Baptiste Passot , Oleg Sinyavskiy , Filip Ponulak , Borja Ibarz Gabardos , Eugene Izhikevich
CPC classification number: B25J9/163 , G05B2219/39271 , G05B2219/39289 , G06N3/008 , G06N3/049
Abstract: 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.
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公开(公告)号：US09789605B2
公开(公告)日：2017-10-17
申请号：US15174858
申请日：2016-06-06
Applicant: Brain Corporation
Inventor： Philip Meier , Jean-Baptiste Passot , Borja Ibarz Gabardos , Patryk Laurent , Oleg Sinyavskiy , Peter O'Connor , Eugene Izhikevich
CPC classification number: B25J9/163 , B25J9/1602 , B25J9/161 , B25J9/1656 , G05B2219/40116 , G05D1/0033 , G05D1/0088 , G05D2201/02 , G06N3/008 , G06N99/005 , Y10S901/01 , Y10S901/46
Abstract: 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.
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