公开(公告)号：US20220212342A1

公开(公告)日：2022-07-07

申请号：US17574760

申请日：2022-01-13

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：US10688657B2

公开(公告)日：2020-06-23

申请号：US16171635

申请日：2018-10-26

Applicant: Brain Corporation

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

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

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：US20200086494A1

公开(公告)日：2020-03-19

申请号：US16582302

申请日：2019-09-25

Applicant: Brain Corporation

Inventor： Dimitry Fisher ,  Cody Griffin ,  Micah Richert ,  Filip Piekniewski ,  Eugene Izhikevich ,  Jayram Moorkanikara Nageswaran ,  John Black

IPC: B25J9/16 ,  G05D1/02 ,  G06K9/00 ,  G06K9/46 ,  G06T7/11 ,  H04N5/33 ,  G05D1/00 ,  G08B21/20

Abstract: Systems and methods for automatic detection of spills are disclosed. In some exemplary implementations, a robot can have a spill detector comprising at least one optical imaging device configured to capture at least one image of a scene containing a spill while the robot moves between locations. The robot can process the at least one image by segmentation. Once the spill has been identified, the robot can then generate an alert indicative at least in part of a recognition of the spill.

公开(公告)号：US10295972B2

公开(公告)日：2019-05-21

申请号：US15143397

申请日：2016-04-29

Applicant: Brain Corporation

Inventor： Patryk Laurent ,  Eugene Izhikevich

IPC: G06F3/01 ,  G06F3/03 ,  H04W8/00 ,  G05B15/02 ,  G06F3/038 ,  H04L12/28 ,  H04M1/725 ,  H04W4/021

Abstract: Gesture recognition systems that are configured to provide users with simplified operation of various controllable devices such as, for example, in-home controllable devices. In one implementation, the gesture recognition system automatically configures itself in order to determine the respective physical locations and/or identities of controllable devices as well as an operating mode for controlling the controllable devices through predetermined gesturing. In some implementations, the gesture recognition systems are also configured to assign boundary areas associated with the controllable devices. Apparatus and methods associated with the gesture recognition systems are also disclosed.

公开(公告)号：US20190061160A1

公开(公告)日：2019-02-28

申请号：US15997397

申请日：2018-06-04

Applicant: BRAIN CORPORATION

Inventor： Dimitry Fisher ,  Cody Griffin ,  Micah Richert ,  Filip Piekniewski ,  Eugene Izhikevich ,  Jayram Moorkanikara Nageswaran ,  John Black

IPC: B25J9/16 ,  G05D1/00 ,  G08B21/20 ,  G06K9/46 ,  G06T7/11 ,  G06K9/00 ,  H04N5/33

Abstract: Systems and methods for automatic detection of spills are disclosed. In some exemplary implementations, a robot can have a spill detector comprising at least one optical imaging device configured to capture at least one image of a scene containing a spill while the robot moves between locations. The robot can process the at least one image by segmentation. Once the spill has been identified, the robot can then generate an alert indicative at least in part of a recognition of the spill.

公开(公告)号：US20190009408A1

公开(公告)日：2019-01-10

申请号：US16131128

申请日：2018-09-14

Applicant: Brain Corporation

Inventor： Botond Szatmary ,  Oyvind Grotmol ,  Eugene Izhikevich ,  Oleg Sinyavskiy

IPC: B25J9/16 ,  B25J9/00 ,  G06N99/00

Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a plurality of predictor apparatus configured to generate motor control output. One predictor may be operable in accordance with a pre-configured process; another predictor may be operable in accordance with a learning process configured based on a teaching signal. An adaptive combiner component may be configured to determine a combined control output controller block may provide control output that may be combined with the predicted control output. The pre-programmed predictor may be configured to operate a robot to perform a task. Based on detection of a context, the controller may adaptively switch to use control output of the learning process to perform the given or another task. User feedback may be utilized during learning.

公开(公告)号：US20180243903A1

公开(公告)日：2018-08-30

申请号：US15967240

申请日：2018-04-30

Applicant: Brain Corporation

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

IPC: B25J9/16 ,  G06N99/00

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.

公开(公告)号：US09597797B2

公开(公告)日：2017-03-21

申请号：US14102410

申请日：2013-12-10

Applicant: BRAIN CORPORATION

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

IPC: G05B19/18 ,  B25J9/16 ,  G05D1/00 ,  G05D1/02 ,  G06N3/00 ,  G06N3/04 ,  G06N99/00

CPC classification number: B25J9/163 ,  B25J9/161 ,  G05B2219/36418 ,  G05B2219/36425 ,  G05B2219/40499 ,  G05D1/005 ,  G05D1/0088 ,  G05D1/0221 ,  G05D2201/02 ,  G06N3/008 ,  G06N3/049 ,  G06N99/005

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：US09533413B2

公开(公告)日：2017-01-03

申请号：US14209826

申请日：2014-03-13

Applicant: Brain Corporation

Inventor： Eugene Izhikevich ,  Dimitry Fisher ,  Jean-Baptiste Passot ,  Heathcliff Hatcher ,  Vadim Polonichko

IPC: B25J9/16 ,  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.

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。

公开(公告)号：US09364950B2

公开(公告)日：2016-06-14

申请号：US14209578

申请日：2014-03-13

Applicant: Brain Corporation

Inventor： Eugene Izhikevich ,  Dimitry Fisher ,  Jean-Baptiste Passot ,  Heathcliff Hatcher ,  Vadim Polonichko

IPC: G05B19/18 ,  B25J9/00 ,  G06N99/00 ,  B25J13/00 ,  B25J9/10 ,  A63H29/22 ,  A63H30/04

CPC classification number: B25J9/0081 ,  A63H29/22 ,  A63H30/04 ,  B25J9/104 ,  B25J13/00 ,  G06N99/005 ,  Y10T74/20305 ,  Y10T74/20311 ,  Y10T74/20317

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。