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    APPARATUS AND METHODS FOR CONTROL OF ROBOT ACTIONS BASED ON CORRECTIVE USER INPUTS
    1.
    发明申请
    APPARATUS AND METHODS FOR CONTROL OF ROBOT ACTIONS BASED ON CORRECTIVE USER INPUTS 有权
    基于正确的用户输入控制机器人动作的装置和方法

    公开(公告)号：US20150217449A1

    公开(公告)日：2015-08-06

    申请号：US14171762

    申请日：2014-02-03

    Applicant: Brain Corporation

    Inventor： Philip Meier ,  Jean-Baptiste Passot ,  Borja Ibarz Gabardos ,  Patryk Laurent ,  Oleg Sinyavskiy ,  Peter O'Connor ,  Eugene Izhikevich

    IPC: B25J9/16 ,  G06N5/04 ,  G06N99/00

    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.

    Abstract translation: 机器人有能力执行广泛的有用任务，如工厂自动化，清洁，交付，辅助护理，环境监测和娱乐。 使机器人在新环境中执行新任务通常需要大量新的软件来编写，通常由专家小组编写。 如果未来的技术可以赋予人们对软件编码有限或不了解的机会来训练机器人来执行定制任务，这将是有价值的。 本发明的一些实施方案提供了响应于用户的校正命令以生成和改进用于基于传感器数据输入确定适当动作的策略的方法和系统。 完成学习后，系统可以通过从感官数据中导出控制命令来生成控制命令。 使用学习的控制策略，机器人可以自主行为。

    Apparatus and methods for control of robot actions based on corrective user inputs
    8.
    发明授权
    Apparatus and methods for control of robot actions based on corrective user inputs 有权
    基于校正用户输入来控制机器人动作的装置和方法

    公开(公告)号：US09358685B2

    公开(公告)日：2016-06-07

    申请号：US14171762

    申请日：2014-02-03

    Applicant: BRAIN CORPORATION

    Inventor： Philip Meier ,  Jean-Baptiste Passot ,  Borja Ibarz Gabardos ,  Patryk Laurent ,  Oleg Sinyavskiy ,  Peter O'Connor ,  Eugene Izhikevich

    IPC: G05B19/04 ,  B25J9/16 ,  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.

    Abstract translation: 机器人有能力执行广泛的有用任务，如工厂自动化，清洁，交付，辅助护理，环境监测和娱乐。 使机器人在新环境中执行新任务通常需要大量新的软件来编写，通常由专家小组编写。 如果未来的技术可以赋予人们对软件编码有限或不了解的机会来训练机器人来执行定制任务，这将是有价值的。 本发明的一些实施方案提供了响应于用户的校正命令以生成和改进用于基于传感器数据输入确定适当动作的策略的方法和系统。 完成学习后，系统可以通过从感官数据中导出控制命令来生成控制命令。 使用学习的控制策略，机器人可以自主行为。

    Trainable convolutional network apparatus and methods for operating a robotic vehicle
    9.
    发明授权
    Trainable convolutional network apparatus and methods for operating a robotic vehicle 有权
    可操作的卷积网络装置和用于操作机器人车辆的方法

    公开(公告)号：US09346167B2

    公开(公告)日：2016-05-24

    申请号：US14265113

    申请日：2014-04-29

    Applicant: Brain Corporation

    Inventor： Peter O'Connor ,  Eugene Izhikevich

    IPC: G05B19/04 ,  G05B19/18 ,  B25J9/16 ,  G06N3/00 ,  G06N3/04 ,  G06N3/08

    CPC classification number: B25J9/1697 ,  B25J9/16 ,  G05D1/0221 ,  G05D2201/0206 ,  G05D2201/0209 ,  G06N3/008 ,  G06N3/049 ,  G06N3/084 ,  Y10S901/01 ,  Y10S901/47

    Abstract: A robotic vehicle may be operated by a learning controller comprising a trainable convolutional network configured to determine control signal based on sensory input. An input network layer may be configured to transfer sensory input into a hidden layer data using a filter convolution operation. Input layer may be configured to transfer sensory input into hidden layer data using a filter convolution. Output layer may convert hidden layer data to a predicted output using data segmentation and a fully connected array of efficacies. During training, efficacy of network connections may be adapted using a measure determined based on a target output provided by a trainer and an output predicted by the network. A combination of the predicted and the target output may be provided to the vehicle to execute a task. The network adaptation may be configured using an error back propagation method. The network may comprise an input reconstruction.

    Abstract translation: 机器人车辆可以由学习控制器操作，学习控制器包括被配置为基于感觉输入来确定控制信号的可训练卷积网络。 输入网络层可以被配置为使用滤波器卷积运算将感觉输入传送到隐藏层数据。 输入层可以被配置为使用滤波器卷积将感觉输入传送到隐藏层数据。 输出层可以使用数据分割和完全连接的功能阵列将隐藏层数据转换为预测输出。 在训练期间，可以使用基于由训练者提供的目标输出和由网络预测的输出确定的度量来调整网络连接的功效。 可以将预测和目标输出的组合提供给车辆以执行任务。 可以使用错误反向传播方法来配置网络适配。 网络可以包括输入重建。

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