公开(公告)号：US20180260685A1

公开(公告)日：2018-09-13

申请号：US15785161

申请日：2017-10-16

Applicant: Brain Corporation

Inventor： Jean-Baptiste Passot ,  Oleg Sinyavskiy ,  Filip Ponulak ,  Patryk Laurent ,  Borja lbarz Gabardos ,  Eugene lzhikevich ,  Vadim Polonichko

IPC: G06N3/04 ,  G06N3/00

CPC classification number: G06N3/049 ,  G06N3/008

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.

公开(公告)号：US10016896B2

公开(公告)日：2018-07-10

申请号：US15199224

申请日：2016-06-30

Applicant: BRAIN CORPORATION

Inventor： Oleg Sinyavskiy ,  Borja Ibarz Gabardos ,  Jean-Baptiste Passot

IPC: G05B19/04 ,  G05B19/18 ,  B25J9/16 ,  B25J5/00

CPC classification number: B25J9/1676 ,  B25J5/007 ,  B25J9/1697 ,  G05B2219/40442 ,  G05B2219/49143 ,  G05B2219/49157 ,  Y10S901/01

Abstract: Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.

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.

公开(公告)号：US09764468B2

公开(公告)日：2017-09-19

申请号：US13842530

申请日：2013-03-15

Applicant: BRAIN Corporation

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

IPC: G06F19/00 ,  B25J9/16 ,  G06N3/00 ,  G06N3/04

CPC classification number: B25J9/163 ,  B25J9/161 ,  G05B2219/39292 ,  G05B2219/39298 ,  G05B2219/40499 ,  G06N3/008 ,  G06N3/049

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.

公开(公告)号：US09579789B2

公开(公告)日：2017-02-28

申请号：US14040520

申请日：2013-09-27

Applicant: BRAIN CORPORATION

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

IPC: B25J9/16 ,  G06N3/00 ,  G06N3/04

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。

公开(公告)号：US09566710B2

公开(公告)日：2017-02-14

申请号：US14070269

申请日：2013-11-01

Applicant: Brain Corporation

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

IPC: G05B19/18 ,  B25J9/16 ,  G06N99/00 ,  G06N3/00 ,  G06N3/04

CPC classification number: B25J9/163 ,  B25J9/161 ,  G05B2219/33034 ,  G05B2219/39289 ,  G05B2219/39298 ,  G06N3/008 ,  G06N3/049 ,  G06N3/08 ,  G06N99/005 ,  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.

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，可以利用机器人来执行以目标轨迹为特征的目标任务。 机器人可以由使用监督学习的用户训练。 用户可以通过经配置以向机器人提供示教信号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，其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。 在一次或多次学习试验期间，可以训练控制器以导航目标轨迹的一部分。 单独的轨迹部分可以在单独的训练试验期间训练。 一些部分可能与执行复杂动作的机器人相关联，并且与更简单的轨迹动作相比可能需要额外的训练试验和/或更密集的训练输入。

公开(公告)号：US20170001309A1

公开(公告)日：2017-01-05

申请号：US15200959

申请日：2016-07-01

Applicant: BRAIN Corporation

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

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

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：US09463571B2

公开(公告)日：2016-10-11

申请号：US14070114

申请日：2013-11-01

Applicant: BRAIN CORPORATION

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

IPC: B25J9/16 ,  G06N3/00 ,  G06N3/04 ,  G05D1/00 ,  G05D1/02 ,  G06N99/00

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。

公开(公告)号：US20160075018A1

公开(公告)日：2016-03-17

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

IPC: B25J9/16 ,  B25J13/08

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。