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    • 24. 发明申请
    • PREDICTIVE ROBOTIC CONTROLLER APPARATUS AND METHODS
    • 预测机器人控制器设备及方法
    • US20160303738A1
    • 2016-10-20
    • US15132003
    • 2016-04-18
    • BRAIN Corporation
    • Patryk LaurentJean-Baptiste PassotOleg SinyavskiyFilip PonulakBorja Ibarz GabardosEugene Izhikevich
    • B25J9/16
    • B25J9/163G05B2219/39271G05B2219/39289G06N3/008G06N3/049
    • 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.
    • 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。
    • 26. 发明授权
    • Robotic control arbitration apparatus and methods
    • 机器人控制仲裁设备及方法
    • US09296101B2
    • 2016-03-29
    • US14040498
    • 2013-09-27
    • BRAIN CORPORATION
    • Patryk LaurentJean-Baptiste PassotEugene Izhikevich
    • B25J9/16G05D1/00G06N3/00G06N3/04
    • B25J9/163G05B2219/40494G05D1/00G05D1/0246G06N3/008G06N3/049Y10S901/03Y10S901/09
    • 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.
    • 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。
    • 27. 发明授权
    • Discrepancy detection apparatus and methods for machine learning
    • 差异检测装置和机器学习方法
    • US09248569B2
    • 2016-02-02
    • US14088258
    • 2013-11-22
    • BRAIN CORPORATION
    • Patryk LaurentJean-Baptiste PassotFilip PonulakEugene Izhikevich
    • B25J9/16
    • B25J9/163G05B13/026G05B2219/40512G05B2219/42058G05D1/0088G05D2201/02G06N3/049G06N99/005
    • 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.
    • 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。
    • 29. 发明申请
    • ROBOTIC TRAINING APPARATUS AND METHODS
    • 机器人训练装置及方法
    • US20140371907A1
    • 2014-12-18
    • US13918338
    • 2013-06-14
    • BRAIN CORPORATION
    • Jean-Baptiste PassotOleg SinyavskiyFilip PonulakPatryk LaurentBorja Ibarz GabardosEugene Izhikevich
    • B25J9/16
    • B25J9/163B25J9/161G05D1/0221G06N3/008G06N3/049G06N3/08
    • 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
    • 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