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

IPC: B25J9/16 ,  G06N99/00

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.

公开(公告)号：US20180272529A1

公开(公告)日：2018-09-27

申请号：US15845832

申请日：2017-12-18

Applicant: Brain Corporation

Inventor： Filip Ponulak ,  Moslem Kazemi ,  Patryk Laurent ,  Oleg Sinyavskiy ,  Eugene Izhikevich

IPC: B25J9/16 ,  G05D1/02 ,  G05D1/00

Abstract: Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.

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

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.

公开(公告)号：US09862092B2

公开(公告)日：2018-01-09

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 ,  G06N3/08 ,  G06N99/00 ,  G06N3/00 ,  B25J13/08 ,  A63H3/20 ,  G06N3/04

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：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

IPC: G05B19/04 ,  B25J9/16 ,  G06N99/00 ,  G06N3/00 ,  G05D1/00

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.

公开(公告)号：US20170203437A1

公开(公告)日：2017-07-20

申请号：US15431465

申请日：2017-02-13

Applicant: Brain Corporation

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

IPC: B25J9/16 ,  G06N3/08

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

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：US09311594B1

公开(公告)日：2016-04-12

申请号：US13623838

申请日：2012-09-20

Applicant: BRAIN CORPORATION

Inventor： Dimitry Fisher ,  Eugene Izhikevich ,  Vadim Polonichko

IPC: G06N5/00 ,  G06F1/00 ,  G06N3/04

CPC classification number: 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：US09275326B2

公开(公告)日：2016-03-01

申请号：US13691554

申请日：2012-11-30

Applicant: Brain Corporation

Inventor： Filip Piekniewski ,  Micah Richert ,  Dimitry Fisher ,  Eugene Izhikevich

IPC: G06F15/18 ,  G06N3/02 ,  G06N3/04

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

Abstract: Apparatus and methods for activity based plasticity in a spiking neuron network adapted to process sensory input. In one embodiment, the plasticity mechanism may be configured for example based on activity of one or more neurons providing feed-forward stimulus and activity of one or more neurons providing inhibitory feedback. When an inhibitory neuron generates an output, inhibitory connections may be potentiated. When an inhibitory neuron receives inhibitory input, the inhibitory connection may be depressed. When the inhibitory input arrives subsequent to the neuron response, the inhibitory connection may be depressed. When input features are unevenly distributed in occurrence, the plasticity mechanism is capable of reducing response rate of neurons that develop receptive fields to more prevalent features. Such functionality may provide network output such that rarely occurring features are not drowned out by more widespread stimulus.

Abstract translation: 适用于处理感觉输入的加标神经元网络中基于活动的可塑性的装置和方法。 在一个实施方案中，可塑性机制可以例如基于提供前馈刺激的一个或多个神经元的活性和提供抑制反馈的一个或多个神经元的活性来配置。 当抑制性神经元产生输出时，可以增强抑制性连接。 当抑制性神经元接受抑制性输入时，可能抑制抑制性连接。 当抑制输入在神经元响应之后到达时，可能抑制抑制性连接。 当输入特征在发生时不均匀分布时，可塑性机制能够将发展接受场的神经元的反应率降低到更普遍的特征。 这样的功能可以提供网络输出，使得很少发生的特征不被更广泛的刺激所淹没。

公开(公告)号：US20150148953A1

公开(公告)日：2015-05-28

申请号：US14088258

申请日：2013-11-22

Applicant: Brain Corporation

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

IPC: B25J9/16

CPC classification number: B25J9/163 ,  G05B13/026 ,  G05B2219/40512 ,  G05B2219/42058 ,  G05D1/0088 ,  G05D2201/02 ,  G06N3/049 ,  G06N99/005

Abstract: A robotic device may comprise an adaptive controller configured to learn to predict consequences of robotic device's actions. During training, the controller may receive a copy of the planned and/or executed motor command and sensory information obtained based on the robot's response to the command. The controller may predict sensory outcome based on the command and one or more prior sensory inputs. The predicted sensory outcome may be compared to the actual outcome. Based on a determination that the prediction matches the actual outcome, the training may stop. Upon detecting a discrepancy between the prediction and the actual outcome, the controller may provide a continuation signal configured to indicate that additional training may be utilized. In some classification implementations, the discrepancy signal may be used to indicate occurrence of novel (not yet learned) objects in the sensory input and/or indicate continuation of training to recognize said objects.

Abstract translation: 机器人设备可以包括被配置为学习预测机器人设备的动作的后果的自适应控制器。 在训练期间，控制器可以接收基于机器人对命令的响应获得的计划和/或执行的电动机命令和感觉信息的副本。 控制器可以基于命令和一个或多个现有的感觉输入来预测感觉结果。 预测的感觉结果可能与实际结果进行比较。 根据预测与实际结果的匹配，培训可能会停止。 在检测到预测和实际结果之间的差异时，控制器可以提供被配置为指示可以利用附加训练的连续信号。 在一些分类实现中，差异信号可以用于指示感觉输入中的新颖（尚未学习）的对象的发生和/或指示用于识别所述对象的训练的继续。

公开(公告)号：US20150127149A1

公开(公告)日：2015-05-07

申请号：US14070114

申请日：2013-11-01

Applicant: BRAIN CORPORATION

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

IPC: B25J9/16

CPC classification number: B25J9/163 ,  G05B2219/33056 ,  G05B2219/40499 ,  G05D1/0088 ,  G05D1/0221 ,  G06N3/008 ,  G06N3/049 ,  G06N99/005 ,  Y10S901/03

Abstract: Robotic devices may be trained by a user guiding the robot along a target trajectory using a correction signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. Training may comprise a plurality of trials. During an initial portion of a trial, the trainer may observe robot's operation and refrain from providing the training input to the robot. Upon observing a discrepancy between the target behavior and the actual behavior during the initial trial portion, the trainer may provide a teaching input (e.g., a correction signal) configured to affect robot's trajectory during subsequent trials. Upon completing a sufficient number of trials, the robot may be capable of navigating the trajectory in absence of the training input.

Abstract translation: 机器人设备可以由用户使用校正信号沿着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，其被配置为基于训练者输入，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。 在试验的初始部分，训练者可以观察机器人的操作，并且避免向机器人提供训练输入。 在观察到初始试验部分期间的目标行为和实际行为之间的差异时，训练者可以提供被配置为在随后的试验期间影响机器人轨迹的示教输入（例如，校正信号）。 在完成足够数量的试验后，机器人可能能够在没有训练输入的情况下导航轨迹。