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公开(公告)号：US10391628B2
公开(公告)日：2019-08-27
申请号：US15474880
申请日：2017-03-30
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot
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.
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公开(公告)号：US20190255703A1
公开(公告)日：2019-08-22
申请号：US16171635
申请日：2018-10-26
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Oleg Sinyavskiy , Jean-Baptiste Passot
Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.
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公开(公告)号：US10166675B2
公开(公告)日：2019-01-01
申请号：US14946589
申请日：2015-11-19
Applicant: BRAIN Corporation
Inventor： Eugene Izhikevich , Dimitry Fisher , Jean-Baptiste Passot , Heathcliff Hatcher , Vadim Polonichko
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.
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公开(公告)号：US20180215039A1
公开(公告)日：2018-08-02
申请号：US15423442
申请日：2017-02-02
Applicant: BRAIN CORPORATION
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Borja Ibarz Gabardos , Diana Vu Le
IPC: B25J9/16
CPC classification number: B25J9/1666 , G05B2219/39082 , G05D1/0044 , G05D1/0274 , G05D2201/0203
Abstract: Systems and methods assisting a robotic apparatus are disclosed. In some exemplary implementations, a robot can encounter situations where the robot cannot proceed and/or does not know with a high degree of certainty it can proceed. Accordingly, the robot can determine that it has encountered an error and/or assist event. In some exemplary implementations, the robot can receive assistance from an operator and/or attempt to resolve the issue itself. In some cases, the robot can be configured to delay actions in order to allow resolution of the error and/or assist event.
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公开(公告)号：US20170329347A1
公开(公告)日：2017-11-16
申请号：US15152425
申请日：2016-05-11
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Andrew Smith , Botond Szatmary , Borja Ibarz Gabardos , Cody Griffin , Jaldert Rombouts , Oleg Sinyavskiy , Eugene Izhikevich
CPC classification number: G05D1/0274 , A47L9/2826 , A47L9/2852 , A47L11/4011 , A47L11/4061 , A47L2201/04 , G05D1/0088 , G05D1/0221 , G05D1/0246 , G05D2201/0203
Abstract: Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.
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Patent Agency Ranking
公开(公告)号：US09792546B2
公开(公告)日：2017-10-17
申请号：US13918298
申请日：2013-06-14
Applicant: BRAIN CORPORATION
Inventor： Jean-Baptiste Passot , Oleg Sinyavskiy , Filip Ponulak , Patryk Laurent , Borja Ibarz Gabardos , Eugene Izhikevich , Vadim Polonichko
Abstract: A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. The remote controller may comprise multiple control elements configured to activate respective portions of the robot platform. Based on training, the remote controller may configure composite controls configured based two or more of control elements. Activation of a composite control may enable the robot to perform a task.
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公开(公告)号：US20160303738A1
公开(公告)日：2016-10-20
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: 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。
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公开(公告)号：US20160279790A1
公开(公告)日：2016-09-29
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
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.
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公开(公告)号：US09296101B2
公开(公告)日：2016-03-29
申请号：US14040498
申请日：2013-09-27
Applicant: BRAIN CORPORATION
Inventor： Patryk Laurent , Jean-Baptiste Passot , Eugene Izhikevich
CPC classification number: B25J9/163 , G05B2219/40494 , G05D1/00 , G05D1/0246 , G06N3/008 , G06N3/049 , Y10S901/03 , Y10S901/09
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。
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公开(公告)号：US09248569B2
公开(公告)日：2016-02-02
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。
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