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    Neural network learning and collaboration apparatus and methods
    25.
    发明授权
    Neural network learning and collaboration apparatus and methods 有权

    公开(公告)号：US09613310B2

    公开(公告)日：2017-04-04

    申请号：US14961522

    申请日：2015-12-07

    Applicant: Brain Corporation

    Inventor： Marius Buibas ,  Eugene M. Izhikevich ,  Botond Szatmary ,  Vadim Polonichko

    IPC: G06N5/00 ,  G06F1/00 ,  G06N3/08 ,  G06N3/04 ,  G06N3/10

    CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/049 ,  G06N3/10

    Abstract: Apparatus and methods for learning and training in neural network-based devices. In one implementation, the devices each comprise multiple spiking neurons, configured to process sensory input. In one approach, alternate heterosynaptic plasticity mechanisms are used to enhance learning and field diversity within the devices. The selection of alternate plasticity rules is based on recent post-synaptic activity of neighboring neurons. Apparatus and methods for simplifying training of the devices are also disclosed, including a computer-based application. A data representation of the neural network may be imaged and transferred to another computational environment, effectively copying the brain. Techniques and architectures for achieve this training, storing, and distributing these data representations are also disclosed.

    Apparatus and methods for removal of learned behaviors in robots
    26.
    发明授权
    Apparatus and methods for removal of learned behaviors in robots 有权
    用于去除机器人学习行为的装置和方法

    公开(公告)号：US09579790B2

    公开(公告)日：2017-02-28

    申请号：US14489373

    申请日：2014-09-17

    Applicant: Brain Corporation

    Inventor： Patryk Laurent ,  Csaba Petre ,  Eugene M. Izhikevich ,  Vadim Polonichko

    IPC: B25J9/16

    CPC classification number: B25J9/163 ,  G05B2219/2642 ,  G05B2219/40116 ,  Y10S901/05

    Abstract: Computerized appliances may be operated by users remotely. In one implementation, a learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

    Abstract translation: 计算机化设备可由用户远程操作。 在一个实现中，可以操作学习控制器设备以确定用户指示与设备的动作之间的关联。 用户指示，例如手势，姿势改变，音频信号可以触发与控制器相关联的事件。 事件可以被链接到被配置为向设备传达命令的多个指令。 学习装置可以接收关于机器人的状态和环境（上下文）的感官输入。 感官输入可用于确定用户指示。 在操作期间，在使用感觉输入确定指示时，控制器可以引起相应指令的执行，以触发设备的动作。 设备动画方法可以使用户能够使用手势，语音命令，姿势改变和/或其他定制的控制元素来操作计算机化的设备。

    INTERFACE FOR USE WITH TRAINABLE MODULAR ROBOTIC APPARATUS
    27.
    发明申请
    INTERFACE FOR USE WITH TRAINABLE MODULAR ROBOTIC APPARATUS 审中-公开
    使用可接受的模块化机器人界面

    公开(公告)号：US20160151912A1

    公开(公告)日：2016-06-02

    申请号：US14958825

    申请日：2015-12-03

    Applicant: BRAIN Corporation

    Inventor： Eugene Izhikevich ,  Dimitry Fisher ,  Jean-Baptiste Passot ,  Heathcliff Hatcher ,  Vadim Polonichko

    IPC: B25J9/16 ,  A63H3/20

    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。

    APPARATUS AND METHODS FOR TRAINING OF ROBOTS
    28.
    发明申请
    APPARATUS AND METHODS FOR TRAINING OF ROBOTS 有权
    装备和训练机器人的方法

    公开(公告)号：US20160096272A1

    公开(公告)日：2016-04-07

    申请号：US14588168

    申请日：2014-12-31

    Applicant: BRAIN CORPORATION

    Inventor： Andrew T. Smith ,  Vadim Polonichko

    IPC: B25J9/16

    CPC classification number: B25J9/163 ,  B25J9/1607 ,  B25J9/1666 ,  B25J9/1697 ,  G05D1/0088 ,  G05D1/0246 ,  G05D2201/02 ,  G06N3/00 ,  G06N3/008 ,  G06N3/049 ,  Y10S901/01 ,  Y10S901/03 ,  Y10S901/09 ,  Y10S901/47

    Abstract: A random k-nearest neighbors (RKNN) approach may be used for regression/classification model wherein the input includes the k closest training examples in the feature space. The RKNN process may utilize video images as input in order to predict motor command for controlling navigation of a robot. In some implementations of robotic vision based navigation, the input space may be highly dimensional and highly redundant. When visual inputs are augmented with data of another modality that is characterized by fewer dimensions (e.g., audio), the visual data may overwhelm lower-dimension data. The RKNN process may partition available data into subsets comprising a given number of samples from the lower-dimension data. Outputs associated with individual subsets may be combined (e.g., averaged). Selection of number of neighbors, subset size and/or number of subsets may be used to trade-off between speed and accuracy of the prediction.

    Abstract translation: 随机k最近邻（RKNN）方法可用于回归/分类模型，其中输入包括特征空间中最接近的k个训练样本。 RKNN过程可以利用视频图像作为输入，以便预测用于控制机器人的导航的马达命令。 在基于机器人视觉的导航的一些实现中，输入空间可以是高度尺寸和高度冗余的。 当视觉输入用另一种具有较少尺寸（例如，音频）特征的模态的数据进行增强时，视觉数据可能会压倒较低维度的数据。 RKNN过程可以将可用数据划分为包含来自较低维数据的给定数量样本的子集。 与各个子集相关联的输出可以组合（例如，平均）。 选择邻居数量，子集大小和/或子集数可用于在速度和预测精度之间进行权衡。

    APPARATUS AND METHODS FOR REMOTELY CONTROLLING ROBOTIC DEVICES
    29.
    发明申请
    APPARATUS AND METHODS FOR REMOTELY CONTROLLING ROBOTIC DEVICES 有权
    用于远程控制机器人装置的装置和方法

    公开(公告)号：US20160075015A1

    公开(公告)日：2016-03-17

    申请号：US14489242

    申请日：2014-09-17

    Applicant: Brain Corporation

    Inventor： Eugene M. Izhikevich ,  Patryk Laurent ,  Csaba Petre ,  Todd Hylton ,  Vadim Polonichko

    IPC: B25J9/16 ,  B25J11/00 ,  B25J9/00 ,  B25J13/00 ,  B25J19/02

    CPC classification number: B25J9/163 ,  B25J9/1697 ,  G05B15/02 ,  G05B2219/2642 ,  G05B2219/40411 ,  H04L12/2803 ,  H04L12/282 ,  H04L2012/2841 ,  H04L2012/2849 ,  Y10S901/05

    Abstract: Computerized appliances may be operated by users remotely. A learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

    Abstract translation: 计算机化设备可由用户远程操作。 可以操作学习控制器设备以确定用户指示与设备的动作之间的关联。 用户指示，例如手势，姿势改变，音频信号可以触发与控制器相关联的事件。 事件可以被链接到被配置为向设备传达命令的多个指令。 学习装置可以接收关于机器人的状态和环境（上下文）的感官输入。 感官输入可用于确定用户指示。 在操作期间，在使用感觉输入确定指示时，控制器可以引起相应指令的执行，以触发设备的动作。 设备动画方法可以使用户能够使用手势，语音命令，姿势改变和/或其他定制的控制元素来操作计算机化的设备。

    OPTICAL DETECTION APPARATUS AND METHODS
    30.
    发明申请
    OPTICAL DETECTION APPARATUS AND METHODS 有权
    光学检测装置及方法

    公开(公告)号：US20160004923A1

    公开(公告)日：2016-01-07

    申请号：US14321736

    申请日：2014-07-01

    Applicant: Brain Corporation

    Inventor： Filip Piekniewski ,  Vadim Polonichko ,  Eugene Izhikevich

    IPC: G06K9/46 ,  H04N5/357 ,  G02B13/00 ,  H04N5/369 ,  H04N5/225 ,  G02B5/20 ,  G06K9/52

    CPC classification number: H04N5/2258 ,  B60W30/09 ,  B60W30/14 ,  B60W30/16 ,  G01C3/08 ,  G02B5/204 ,  G02B7/38 ,  G02B13/0015 ,  G02B27/0075 ,  G06K9/00624 ,  G06K9/00664 ,  G06K9/00791 ,  G06K9/00825 ,  G06K9/4614 ,  G06K9/522 ,  H04N5/2253

    Abstract: An optical object detection apparatus and associated methods. The apparatus may comprise a lens (e.g., fixed-focal length wide aperture lens) and an image sensor. The fixed focal length of the lens may correspond to a depth of field area in front of the lens. When an object enters the depth of field area (e.g., sue to a relative motion between the object and the lens) the object representation on the image sensor plane may be in-focus. Objects outside the depth of field area may be out of focus. In-focus representations of objects may be characterized by a greater contrast parameter compared to out of focus representations. One or more images provided by the detection apparatus may be analyzed in order to determine useful information (e.g., an image contrast parameter) of a given image. Based on the image contrast meeting one or more criteria, a detection indication may be produced.

    Abstract translation: 一种光学物体检测装置及相关方法。 该装置可以包括透镜（例如，固定焦距宽孔径透镜）和图像传感器。 透镜的固定焦距可以对应于透镜前方的景深区域。 当物体进入景深区域（例如，起诉到物体和透镜之间的相对运动）时，图像传感器平面上的对象表示可能是对焦的。 景深外的物体可能会失焦。 与焦点外表示相比，物体的对焦表示可以表现为较大的对比度参数。 可以分析由检测装置提供的一个或多个图像，以便确定给定图像的有用信息（例如，图像对比度参数）。 基于符合一个或多个标准的图像对比，可以产生检测指示。

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