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公开(公告)号：US09242372B2
公开(公告)日：2016-01-26
申请号：US13907734
申请日：2013-05-31
Applicant: BRAIN CORPORATION
Inventor： Patryk Laurent , Jean-Baptiste Passot , Mark Wildie , Eugene M. Izhikevich
IPC: B25J9/16 , G05B19/409 , G06N3/04
CPC classification number: B25J9/161 , G05B19/409 , G05B2219/36136 , G05B2219/36137 , G06N3/049 , Y10S901/02 , Y10S901/28
Abstract: Apparatus and methods for training of robotic devices. 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. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.
Abstract translation: 用于训练机器人装置的装置和方法。 机器人可以由使用者使用控制信号沿目标轨迹引导机器人的用户进行训练。 机器人可以包括自适应控制器。 控制器可以被配置为基于用户指导，感觉输入和性能测量来产生控制命令。 用户可以通过自适应配置的遥控器与机器人接口。 遥控器可以包括由用户根据机器人的表型和/或操作配置来配置的移动设备。 遥控器可以检测机器人表型和/或操作配置的变化。 可以基于检测到的表型和/或操作变化来重新配置遥控器的用户界面。
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公开(公告)号：US20150258679A1
公开(公告)日：2015-09-17
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
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。
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83.发明申请APPARATUS AND METHODS FOR OPERATING ROBOTIC DEVICES USING SELECTIVE STATE SPACE TRAINING 有权使用选择性空间训练操作机器人设备的装置和方法公开(公告)号：US20150127155A1
公开(公告)日：2015-05-07
申请号：US14070269
申请日：2013-11-01
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Oleg Sinyavskiy , Eugene Izhikevich
IPC: B25J9/16
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。
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公开(公告)号：US09008840B1
公开(公告)日：2015-04-14
申请号：US13866975
申请日：2013-04-19
Applicant: Brain Corporation
Inventor： Filip Ponulak , Jean-Baptiste Passot , Eugene Izhikevich , Olivier Coenen
CPC classification number: B25J9/163 , B25J9/161 , G05B13/0265 , G06N3/008 , G06N3/02 , G06N3/049 , G06N99/005
Abstract: Framework may be implemented for transferring knowledge from an external agent to a robotic controller. In an obstacle avoidance/target approach application, the controller may be configured to determine a teaching signal based on a sensory input, the teaching signal conveying information associated with target action consistent with the sensory input, the sensory input being indicative of the target/obstacle. The controller may be configured to determine a control signal based on the sensory input, the control signal conveying information associated with target approach/avoidance action. The controller may determine a predicted control signal based on the sensory input and the teaching signal, the predicted control conveying information associated with the target action. The control signal may be combined with the predicted control in order to cause the robotic apparatus to execute the target action.
Abstract translation: 可以实现框架以将知识从外部代理传送到机器人控制器。 在避障/目标方法应用中，控制器可以被配置为基于感觉输入来确定教学信号，所述教学信号传达与感觉输入一致的目标动作相关联的信息，感觉输入指示目标/障碍物 。 控制器可以被配置为基于感觉输入来确定控制信号，所述控制信号传达与目标接近/回避动作相关联的信息。 控制器可以基于感觉输入和教学信号来确定预测的控制信号，预测的控制传达与目标动作相关联的信息。 控制信号可以与预测控制组合，以使机器人装置执行目标动作。
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公开(公告)号：US20140358284A1
公开(公告)日：2014-12-04
申请号：US13907734
申请日：2013-05-31
Applicant: BRAIN CORPORATION
Inventor： Patryk Laurent , Jean-Baptiste Passot , Mark Wildie , Eugene M. Izhikevich
IPC: B25J9/16
CPC classification number: B25J9/161 , G05B19/409 , G05B2219/36136 , G05B2219/36137 , G06N3/049 , Y10S901/02 , Y10S901/28
Abstract: Apparatus and methods for training of robotic devices. 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. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.
Abstract translation: 用于训练机器人装置的装置和方法。 机器人可以由使用者使用控制信号沿目标轨迹引导机器人的用户进行训练。 机器人可以包括自适应控制器。 控制器可以被配置为基于用户指导，感觉输入和性能测量来产生控制命令。 用户可以通过自适应配置的遥控器与机器人接口。 遥控器可以包括由用户根据机器人的表型和/或操作配置来配置的移动设备。 遥控器可以检测机器人表型和/或操作配置的变化。 可以基于检测到的表型和/或操作变化来重新配置遥控器的用户界面。
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Patent Agency Ranking
86.发明授权公开(公告)号：US12164299B2
公开(公告)日：2024-12-10
申请号：US17149951
申请日：2021-01-15
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Michal Garmulewicz
Abstract: Systems and methods for optimizing robotic route planning are disclosed in relation to autonomous navigation of sharp turns, narrow passageways, and/or a sharp turn into a narrow passageway. Robots navigating a route comprising any of the above run the risk of colliding with environment obstacles when executing these maneuvers. Accordingly, systems and methods for improving robotic route planning are necessary within the art and are disclosed herein.
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公开(公告)号：US11467602B2
公开(公告)日：2022-10-11
申请号：US16168368
申请日：2018-10-23
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Andrew Smith , Botond Szatmary , Borja Ibarz Gabardos , Cody Griffin , Jaldert Rombouts , Oleg Sinyavskiy , Eugene Izhikevich
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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公开(公告)号：US11331800B2
公开(公告)日：2022-05-17
申请号：US16908038
申请日：2020-06-22
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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89.发明授权公开(公告)号：US11279025B2
公开(公告)日：2022-03-22
申请号：US16235250
申请日：2018-12-28
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Oleg Sinyavskiy , Eugene Izhikevich
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.
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公开(公告)号：US20220083058A1
公开(公告)日：2022-03-17
申请号：US17461153
申请日：2021-08-30
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Jaldert Rombouts , Cody Griffin , John Black
Abstract: Systems and methods for initializing a robot to autonomously travel a route are disclosed. In some exemplary implementations, a robot can detect an initialization object and then determine its position relative to that initialization object. The robot can then learn a route by user demonstration, where the robot associates actions along that route with positions relative to the initialization object. The robot can later detect the initialization object again and determine its position relative to that initialization object. The robot can then autonomously navigate the learned route, performing actions associated with positions relative to the initialization object.
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