公开(公告)号：US09218563B2

公开(公告)日：2015-12-22

申请号：US13660982

申请日：2012-10-25

Applicant: Brain Corporation

Inventor： Botond Szatmary ,  Micah Richert ,  Eugene Izhikevich ,  Jayram Moorkanikara Nageswaran ,  Filip Piekniewski ,  Sach Sokol ,  Csaba Petre

IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/02 ,  G06F15/00 ,  G06F17/00 ,  G06N3/10

CPC classification number: G06N3/02 ,  G06N3/049 ,  G06N3/063 ,  G06N3/088

Abstract: Apparatus and methods for salient feature detection by a spiking neuron network. The network may comprise feature-specific units capable of responding to different objects (red and green color). The plasticity mechanism of the network may be configured based on difference between two similarity measures related to activity of different unit types obtained during network training. One similarity measure may be based on activity of units of the same type (red). Another similarity measure may be based on activity of units of one type (red) and another type (green). Similarity measures may comprise a cross-correlogram and/or mutual information determined over an activity window. During network operation, the activity based plasticity mechanism may be used to potentiate connections between units of the same type (red-red). The plasticity mechanism may be used to depress connections between units of different types (red-green). The plasticity mechanism may effectuate detection of salient features in the input.

Abstract translation: 通过尖峰神经元网络显着特征检测的装置和方法。 网络可以包括能够响应于不同对象（红色和绿色）的特征单元。 网络的可塑性机制可以基于网络训练过程中获得的不同单元类型的活动相关的两个相似性度量之间的差异进行配置。 一个相似性度量可以基于相同类型（红色）的单元的活动。 另一种相似性度量可以基于一种类型（红色）和另一种类型（绿色）的单位的活动。 相似性度量可以包括在活动窗口上确定的交叉相关图和/或相互信息。 在网络运行期间，基于活动的可塑性机制可用于加强相同类型（红 - 红）单元之间的连接。 可塑性机制可用于抑制不同类型（红 - 绿）单元之间的连接。 可塑性机制可能会影响输入中突出特征的检测。

公开(公告)号：US20150217449A1

公开(公告)日：2015-08-06

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: B25J9/16 ,  G06N5/04 ,  G06N99/00

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：US20150127154A1

公开(公告)日：2015-05-07

申请号：US14070239

申请日：2013-11-01

Applicant: Brain Corporation

Inventor： Jean-Baptiste Passot ,  Oleg Sinyavskiy ,  Eugene Izhikevich

IPC: B25J9/16

CPC classification number: B25J9/163 ,  B25J9/161 ,  G05B2219/33034 ,  G05B2219/39289 ,  G05B2219/39298 ,  G06N3/008 ,  G06N3/049 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  Y10S901/03

Abstract: Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.

Abstract translation: 用于训练和控制例如机器人装置的装置和方法。 在一个实现中，可以由使用监督学习的用户训练机器人。 用户可能无法同时控制机器人的所有自由度。 用户可以通过配置成选择和操作机器人的执行器补码的子集的控制装置与机器人接口。 机器人可以包括包括神经元网络的自适应控制器。 自适应控制器可以被配置为基于学习过程的用户输入和输出来生成致动器控制命令。 自适应控制器的训练可以包括部分组训练。 用户可以训练自适应控制器来操作第一致动器子集。 在学习操作第一子集之后，可以训练自适应控制器以基于经由控制装置的用户输入来操作另一自由度子集。

公开(公告)号：US20140244557A1

公开(公告)日：2014-08-28

申请号：US13774934

申请日：2013-02-22

Applicant: BRAIN CORPORATION

Inventor： Filip Piekniewski ,  Micah Richert ,  Eugene Izhikevich

IPC: G06N3/02

CPC classification number: G06N3/08 ,  G06N3/02 ,  G06N3/049 ,  G06N3/10 ,  G06N99/005

Abstract: Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one approach, the plasticity mechanism of a connection may comprise a causal potentiation portion and an anti-causal portion. The anti-causal portion, corresponding to the input into a neuron occurring after the neuron response, may be configured based on the prior activity of the neuron. When the neuron is in low activity state, the connection, when active, may be potentiated by a base amount. When the neuron activity increases due to another input, the efficacy of the connection, if active, may be reduced proportionally to the neuron activity. Such functionality may enable the network to maintain strong, albeit inactive, connections available for use for extended intervals.

公开(公告)号：US11224971B2

公开(公告)日：2022-01-18

申请号：US16447261

申请日：2019-06-20

Applicant: Brain Corporation

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

IPC: B25J9/16 ,  G06N3/00 ,  G06N3/04

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.

公开(公告)号：US20190217467A1

公开(公告)日：2019-07-18

申请号：US16235250

申请日：2018-12-28

Applicant: Brain Corporation

Inventor： Jean-Baptiste Passot ,  Oleg Sinyavskiy ,  Eugene Izhikevich

IPC: B25J9/16 ,  G06N3/08 ,  G06N20/00

Abstract: Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.

公开(公告)号：US10322507B2

公开(公告)日：2019-06-18

申请号：US15785181

申请日：2017-10-16

Applicant: Brain Corporation

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

IPC: B25J9/00 ,  B25J9/16 ,  G06N20/00 ,  G06N3/00 ,  G05D1/00 ,  G05B13/02

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.

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

IPC: B25J9/16 ,  G06N20/00 ,  B25J13/08 ,  A63H3/20 ,  G06N3/00

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：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

IPC: G05G9/00 ,  G05G13/00 ,  B25J9/00 ,  B25J9/10 ,  B25J9/08

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.

公开(公告)号：US20180099409A1

公开(公告)日：2018-04-12

申请号：US15785181

申请日：2017-10-16

Applicant: Brain Corporation

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

IPC: B25J9/16 ,  G05D1/00 ,  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.