公开(公告)号：US09384443B2

公开(公告)日：2016-07-05

申请号：US13918338

申请日：2013-06-14

Applicant: BRAIN CORPORATION

Inventor： Jean-Baptiste Passot ,  Oleg Sinyavskiy ,  Filip Ponulak ,  Patryk Laurent ,  Borja Ibarz Gabardos ,  Eugene Izhikevich

IPC: B25J9/16 ,  G06N3/00 ,  G05D1/02 ,  G06N3/04

CPC classification number: B25J9/163 ,  B25J9/161 ,  G05D1/0221 ,  G06N3/008 ,  G06N3/049 ,  G06N3/08

Abstract: 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.

Abstract translation: 用于训练机器人装置的装置和方法。 机器人装置可以由用户使用输入信号沿目标轨迹引导机器人进行训练。 机器人设备可以包括自适应控制器，其被配置为基于用户引导，感觉输入和/或性能测量中的一个或多个来产生控制命令。 培训可能包括多项试验。 在第一次试用期间，用户输入可能足以使机器人完成轨迹。 在随后的试验期间，用户和机器人的控制器可以协作，以便可以减少用户输入，同时可以增加机器人控制。 在训练期间来自用户和机器人控制器的个人贡献可能不足以（完全用于完成任务）。 在学习之后，用户的知识可以传送到机器人的控制器，以便在没有用户的后续输入的情况下执行任务执行。

公开(公告)号：US09358685B2

公开(公告)日：2016-06-07

申请号：US14171762

申请日：2014-02-03

Applicant: BRAIN CORPORATION

Inventor： Philip Meier ,  Jean-Baptiste Passot ,  Borja Ibarz Gabardos ,  Patryk Laurent ,  Oleg Sinyavskiy ,  Peter O'Connor ,  Eugene Izhikevich

IPC: G05B19/04 ,  B25J9/16 ,  G06N99/00 ,  G06N3/00

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.

Abstract translation: 机器人有能力执行广泛的有用任务，如工厂自动化，清洁，交付，辅助护理，环境监测和娱乐。 使机器人在新环境中执行新任务通常需要大量新的软件来编写，通常由专家小组编写。 如果未来的技术可以赋予人们对软件编码有限或不了解的机会来训练机器人来执行定制任务，这将是有价值的。 本发明的一些实施方案提供了响应于用户的校正命令以生成和改进用于基于传感器数据输入确定适当动作的策略的方法和系统。 完成学习后，系统可以通过从感官数据中导出控制命令来生成控制命令。 使用学习的控制策略，机器人可以自主行为。

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。

公开(公告)号：US09346167B2

公开(公告)日：2016-05-24

申请号：US14265113

申请日：2014-04-29

Applicant: Brain Corporation

Inventor： Peter O'Connor ,  Eugene Izhikevich

IPC: G05B19/04 ,  G05B19/18 ,  B25J9/16 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08

CPC classification number: B25J9/1697 ,  B25J9/16 ,  G05D1/0221 ,  G05D2201/0206 ,  G05D2201/0209 ,  G06N3/008 ,  G06N3/049 ,  G06N3/084 ,  Y10S901/01 ,  Y10S901/47

Abstract: A robotic vehicle may be operated by a learning controller comprising a trainable convolutional network configured to determine control signal based on sensory input. An input network layer may be configured to transfer sensory input into a hidden layer data using a filter convolution operation. Input layer may be configured to transfer sensory input into hidden layer data using a filter convolution. Output layer may convert hidden layer data to a predicted output using data segmentation and a fully connected array of efficacies. During training, efficacy of network connections may be adapted using a measure determined based on a target output provided by a trainer and an output predicted by the network. A combination of the predicted and the target output may be provided to the vehicle to execute a task. The network adaptation may be configured using an error back propagation method. The network may comprise an input reconstruction.

Abstract translation: 机器人车辆可以由学习控制器操作，学习控制器包括被配置为基于感觉输入来确定控制信号的可训练卷积网络。 输入网络层可以被配置为使用滤波器卷积运算将感觉输入传送到隐藏层数据。 输入层可以被配置为使用滤波器卷积将感觉输入传送到隐藏层数据。 输出层可以使用数据分割和完全连接的功能阵列将隐藏层数据转换为预测输出。 在训练期间，可以使用基于由训练者提供的目标输出和由网络预测的输出确定的度量来调整网络连接的功效。 可以将预测和目标输出的组合提供给车辆以执行任务。 可以使用错误反向传播方法来配置网络适配。 网络可以包括输入重建。

公开(公告)号：US09314924B1

公开(公告)日：2016-04-19

申请号：US13918620

申请日：2013-06-14

Applicant: BRAIN CORPORATION

Inventor： Patryk Laurent ,  Jean-Baptiste Passot ,  Oleg Sinyavskiy ,  Filip Ponulak ,  Borja Ibarz Gabardos ,  Eugene Izhikevich

IPC: G05B19/18 ,  B25J9/16

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.

Abstract translation: 机器人设备可以由用户使用输入信号沿着目标动作轨迹引导机器人进行训练。 机器人设备可以包括自适应控制器，其被配置为基于用户引导，感觉输入，性能测量和/或其他信息中的一个或多个来产生控制信号。 培训可以包括多个试验，其中对于给定的上下文，用户和机器人的控制器可以协作以在上下文和目标动作之间建立关联。 在开发关联时，自适应控制器可以能够在用户输入之前和/或代替用户输入时产生控制信号和/或动作指示。 由控制器获得的预测控制功能可以实现机器人设备的自主操作，从而避免需要持续的用户指导。

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。

公开(公告)号：US09193075B1

公开(公告)日：2015-11-24

申请号：US13689717

申请日：2012-11-29

Applicant: BRAIN Corporation

Inventor： Benjamin Neil Cipollini ,  Eugene Izhikevich

IPC: G06N3/04 ,  B25J13/08 ,  G06K9/40 ,  G06N3/02

CPC classification number: B25J13/08 ,  G06K9/40 ,  G06N3/02 ,  G06N3/049 ,  G06N3/10 ,  G06T7/20 ,  Y10S901/09

Abstract: 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.

Abstract translation: 移动平台的光流可以编码成脉冲输出。 由于平台自动运动引起的光流贡献可能被取消。 取消可以通过（i）将平台运动编码成脉冲输出来实现; 和（ii）抑制被配置为基于自身运动而发生的光流分量光流的神经元的脉冲产生。 经编码的运动可以经由一个或多个连接耦合到光流编码器。 连接传播延迟可以在没有障碍物的编码器校准期间被配置，以便提供系统特定的延迟矩阵。 抑制可以基于通过校准连接到光流编码器神经元的运动加标信号的一致到达。 可以利用重合运动脉冲到达以实现两个或更多个向量属性的添加。

公开(公告)号：US20150258683A1

公开(公告)日：2015-09-17

申请号：US14209826

申请日：2014-03-13

Applicant: BRAIN CORPORATION

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

IPC: B25J9/16 ,  G06N99/00

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.

公开(公告)号：US09047568B1

公开(公告)日：2015-06-02

申请号：US13623820

申请日：2012-09-20

Applicant: BRAIN CORPORATION

Inventor： Dimitry Fisher ,  Botond Szatmary ,  Eugene Izhikevich

IPC: G06N5/02 ,  G06N3/04

CPC classification number: G06N3/0472 ,  G06N3/049

Abstract: Sensory encoder may be implemented. Visual encoder apparatus may comprise spiking neuron network configured to receive photodetector input. Excitability of neurons may be adjusted and output spike may be generated based on the input. When neurons generate spiking response, spiking threshold may be dynamically adapted to produce desired output rate. The encoder may dynamically adapt its input range to match statistics of the input and to produce output spikes at an appropriate rate and/or latency. Adaptive input range adjustment and/or spiking threshold adjustment collaborate to enable recognition of features in sensory input of varying dynamic range.

Abstract translation: 可以实现感觉编码器。 可视编码器装置可以包括配置成接收光电检测器输入的加标神经元网络。 可以调整神经元的兴奋性，并且可以基于输入产生输出尖峰。 当神经元产生尖峰响应时，尖峰阈值可以动态地适应以产生期望的输出速率。 编码器可以动态地调整其输入范围以匹配输入的统计量，并以适当的速率和/或等待时间产生输出尖峰。 自适应输入范围调整和/或加标阈值调整协作可以识别不同动态范围的感官输入中的特征。

公开(公告)号：US20150094850A1

公开(公告)日：2015-04-02

申请号：US14040520

申请日：2013-09-27

Applicant: BRAIN CORPORATION

Inventor： Jean-Baptiste Passot ,  Patryk Laurent ,  Eugene Izhikevich

IPC: B25J9/16

CPC classification number: B25J9/163 ,  G05B2219/39271 ,  G05B2219/39307 ,  G05B2219/40499 ,  G06N3/008 ,  G06N3/049 ,  Y10S901/03

Abstract: Apparatus and methods for arbitration of control signals for robotic devices. A robotic device may comprise an adaptive controller comprising a plurality of predictors configured to provide multiple predicted control signals based on one or more of the teaching input, sensory input, and/or performance. The predicted control signals may be configured to cause two or more actions that may be in conflict with one another and/or utilize a shared resource. An arbitrator may be employed to select one of the actions. The selection process may utilize a WTA, reinforcement, and/or supervisory mechanisms in order to inhibit one or more predicted signals. The arbitrator output may comprise target state information that may be provided to the predictor block. Prior to arbitration, the predicted control signals may be combined with inputs provided by an external control entity in order to reduce learning time.

Abstract translation: 用于对机器人装置的控制信号进行仲裁的装置和方法。 机器人设备可以包括自适应控制器，其包括多个预测器，其被配置为基于教学输入，感觉输入和/或性能中的一个或多个来提供多个预测控制信号。 预测的控制信号可以被配置为引起可能彼此冲突和/或利用共享资源的两个或更多个动作。 仲裁员可以用来选择一个动作。 选择过程可以利用WTA，加强和/或监督机制来抑制一个或多个预测信号。 仲裁器输出可以包括可以提供给预测器块的目标状态信息。 在仲裁之前，预测的控制信号可以与由外部控制实体提供的输入组合以减少学习时间。