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公开(公告)号：US20190143505A1
公开(公告)日：2019-05-16
申请号：US16199582
申请日：2018-11-26
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot , Heathcliff Hatcher , Vadim Polonichko
Abstract: 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.
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公开(公告)号：US20190005659A1
公开(公告)日：2019-01-03
申请号：US16104622
申请日：2018-08-17
Applicant: BRAIN CORPORATION
Inventor： Filip Piekniewski , Micah Richert , Dimitry Fisher
IPC: G06T7/292
Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.
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公开(公告)号：US09987743B2
公开(公告)日：2018-06-05
申请号：US14208709
申请日：2014-03-13
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot
CPC classification number: B25J9/0009 , B25J9/08 , B25J9/104 , Y10T74/20305 , Y10T74/20311
Abstract: 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.
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4.发明申请公开(公告)号：US20160086052A1
公开(公告)日：2016-03-24
申请号：US14637191
申请日：2015-03-03
Applicant: Brain Corporation
Inventor： Filip Piekniewski , Micah Richert , Dimitry Fisher
CPC classification number: G06T7/292 , G06K9/3241 , G06K9/4623 , G06K9/4642 , G06K9/4652 , G06K9/4671 , G06K9/48 , G06T7/20 , G06T7/246 , G06T7/90 , G06T2207/10024 , G06T2207/20164 , G06T2207/30241 , G06T2215/16 , H04N9/7973
Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.
Abstract translation: 用于检测和利用数字图像显着性的装置和方法。 在一个实现中，可以基于像素特性的分析来检测显着对象。 最常出现的像素值可能被认为是显着的。 图像中的像素值可以与参考进行比较。 可以基于参考颜色和像素颜色之间的差来确定颜色距离。 当确定多通道图像中的显着性时，可以缩放各个图像通道。 可以分析高显着性的区域以确定对象位置，形状和/或颜色。 为了提高检测性能（例如，减少误报数量），多重显着图可以被相加或乘法组合。 本文描述的方法可以利用较少的确定资源来实现对物体的鲁棒跟踪。 下面描述的方法的有效实现可以允许它们例如在机器人（或自主车辆）或移动确定平台上使用。
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5.发明申请SALIENT FEATURES TRACKING APPARATUS AND METHODS USING VISUAL INITIALIZATION 审中-公开追踪特征跟踪设备和使用视觉初始化的方法公开(公告)号：US20160086050A1
公开(公告)日：2016-03-24
申请号：US14637138
申请日：2015-03-03
Applicant: Brain Corporation
Inventor： Filip Piekniewski , Micah Richert , Dimitry Fisher
CPC classification number: G06T7/292 , G06K9/3241 , G06K9/4623 , G06K9/4642 , G06K9/4652 , G06K9/4671 , G06K9/48 , G06T7/20 , G06T7/246 , G06T7/90 , G06T2207/10024 , G06T2207/20164 , G06T2207/30241 , G06T2215/16 , H04N9/7973
Abstract: Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.
Abstract translation: 用于检测和利用数字图像显着性的装置和方法。 在一个实现中，可以基于像素特性的分析来检测显着对象。 最常出现的像素值可能被认为是显着的。 图像中的像素值可以与参考进行比较。 可以基于参考颜色和像素颜色之间的差来确定颜色距离。 当确定多通道图像中的显着性时，可以缩放各个图像通道。 可以分析高显着性的区域以确定对象位置，形状和/或颜色。 为了提高检测性能（例如，减少误报数量），多重显着图可以被相加或乘法组合。 本文描述的方法可以利用较少的确定资源来实现对物体的鲁棒跟踪。 下面描述的方法的有效实现可以允许它们例如在机器人（或自主车辆）或移动确定平台上使用。
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Patent Agency Ranking
公开(公告)号：US20160075018A1
公开(公告)日：2016-03-17
申请号：US14946589
申请日：2015-11-19
Applicant: BRAIN Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot , Heathcliff Hatcher , Vadim Polonichko
CPC classification number: B25J9/163 , A63H3/20 , B25J9/1694 , B25J13/08 , G06N3/008 , G06N3/049 , G06N99/005 , Y10S901/02 , Y10S901/04 , Y10S901/09 , Y10S901/50
Abstract: 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.
Abstract translation: 具有接受训练控制的人造智能的模块化机器人装置的装置和方法。 在一个实现中，模块化机器人设备架构可以用于在与机器人主体分离的自主模块中提供全部或最高成本的组件。 自主模块可以包括可以连接到机器人身体的可控元件的控制器，电源，致动器。 控制器可将玩具的四肢定位在目标位置。 用户可以利用触觉训练方法，以使机器人玩具能够执行目标动作。 本公开的模块化配置使得用户可以在使用由自主模块提供的硬件的同时，用另一个（例如，长颈鹿）来替换一个玩具体（例如熊）。 模块化架构可以使用户能够购买单个AM以用于多个机器人体，从而降低总拥有成本。
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公开(公告)号：US20150258679A1
公开(公告)日：2015-09-17
申请号：US14209578
申请日：2014-03-13
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot , Heathcliff Hatcher , Vadim Polonichko
CPC classification number: B25J9/0081 , A63H29/22 , A63H30/04 , B25J9/104 , B25J13/00 , G06N99/005 , Y10T74/20305 , Y10T74/20311 , Y10T74/20317
Abstract: 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.
Abstract translation: 具有接受训练控制的人造智能的模块化机器人装置的装置和方法。 在一个实现中，模块化机器人设备架构可以用于在与机器人主体分离的自主模块中提供全部或最高成本的组件。 自主模块可以包括可以连接到机器人身体的可控元件的控制器，电源，致动器。 控制器可将玩具的四肢定位在目标位置。 用户可以利用触觉训练方法，以使机器人玩具能够执行目标动作。 本公开的模块化配置使得用户可以在使用由自主模块提供的硬件的同时，用另一个（例如，长颈鹿）来替换一个玩具体（例如熊）。 模块化架构可以使用户能够购买单个AM以用于多个机器人体，从而降低总拥有成本。
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公开(公告)号：US10464213B2
公开(公告)日：2019-11-05
申请号：US15997397
申请日：2018-06-04
Applicant: BRAIN CORPORATION
Inventor： Dimitry Fisher , Cody Griffin , Micah Richert , Filip Piekniewski , Eugene Izhikevich , Jayram Moorkanikara Nageswaran , John Black
Abstract: Systems and methods for automatic detection of spills are disclosed. In some exemplary implementations, a robot can have a spill detector comprising at least one optical imaging device configured to capture at least one image of a scene containing a spill while the robot moves between locations. The robot can process the at least one image by segmentation. Once the spill has been identified, the robot can then generate an alert indicative at least in part of a recognition of the spill.
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公开(公告)号：US10391628B2
公开(公告)日：2019-08-27
申请号：US15474880
申请日：2017-03-30
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot
Abstract: 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.
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公开(公告)号：US10166675B2
公开(公告)日：2019-01-01
申请号：US14946589
申请日：2015-11-19
Applicant: BRAIN Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot , Heathcliff Hatcher , Vadim Polonichko
Abstract: 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.
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