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    APPARATUS AND METHODS FOR CONTROL OF ROBOT ACTIONS BASED ON CORRECTIVE USER INPUTS
    11.
    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。

    REDUCED DEGREE OF FREEDOM ROBOTIC CONTROLLER APPARATUS AND METHODS
    12.
    发明申请
    REDUCED DEGREE OF FREEDOM ROBOTIC CONTROLLER APPARATUS AND METHODS 审中-公开
    自由度机器人控制器设备和方法的降低程度

    公开(公告)号：US20150127154A1

    公开(公告)日：2015-05-07

    申请号：US14070239

    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 ,  G06N3/063 ,  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.

    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，可以由使用监督学习的用户训练机器人。 用户可能无法同时控制机器人的所有自由度。 用户可以通过配置成选择和操作机器人的执行器补码的子集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。 自适应控制器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。 自适应控制器的训练可以包括部分组训练。 用户可以训练自适应控制器来操作第一致动器子集。 在学习操作第一子集之后，可以训练自适应控制器以基于经由控制装置的用户输入来操作另一自由度子集。

    APPARATUS AND METHODS FOR CONTROLLING OF ROBOTIC DEVICES
    13.
    发明申请
    APPARATUS AND METHODS FOR CONTROLLING OF ROBOTIC DEVICES 审中-公开
    用于控制机器人装置的装置和方法

    公开(公告)号：US20150032258A1

    公开(公告)日：2015-01-29

    申请号：US13953595

    申请日：2013-07-29

    Applicant: BRAIN CORPORATION

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

    IPC: B25J9/16

    CPC classification number: B25J9/1656 ,  G05B2219/35444 ,  G05B2219/40116 ,  G06N3/008 ,  G06N3/049

    Abstract: A robot may be trained based on cooperation between an operator and a trainer. During training, the operator may control the robot using a plurality of control instructions. The trainer may observe movements of the robot and generate a plurality of control commands, such as gestures, sound and/or light wave modulation. Control instructions may be combined with the trainer commands via a learning process in order to develop an association between the two. During operation, the learning process may generate one or more control instructions based on one or more gesture by the trainer. One or both the trainer or the operator may comprise a human, and/or computerized entity.

    Abstract translation: 可以基于操作者和训练者之间的协作训练机器人。 在训练期间，操作员可以使用多个控制指令来控制机器人。 训练者可以观察机器人的运动并产生多个控制命令，例如手势，声音和/或光波调制。 控制指令可以通过学习过程与训练器命令组合，以便在两者之间建立关联。 在操作期间，学习过程可以基于训练者的一个或多个手势产生一个或多个控制指令。 训练者或操作员中的一个或两个可以包括人类和/或计算机化的实体。

    SYSTEMS AND METHODS FOR PRECISE NAVIGATION OF AUTONOMOUS DEVICES
    18.
    发明申请
    SYSTEMS AND METHODS FOR PRECISE NAVIGATION OF AUTONOMOUS DEVICES 有权

    公开(公告)号：US20220026911A1

    公开(公告)日：2022-01-27

    申请号：US17409274

    申请日：2021-08-23

    Applicant: Brain Corporation

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

    IPC: G05D1/02

    Abstract: The safe operation and navigation of robots is an active research topic for many real-world applications, such as the automation of large industrial equipment. This technological field often requires heavy machines with arbitrary shapes to navigate very close to obstacles, a challenging and largely unsolved problem. To address this issue, a new planning architecture is developed that allows wheeled vehicles to navigate safely and without human supervision in cluttered environments. The inventive methods and systems disclosed herein belong to the Model Predictive Control (MPC) family of local planning algorithms. The technological features disclosed herein works in the space of two-dimensional (2D) occupancy grids and plans in motor command space using a black box forward model for state inference. Compared to the conventional methods and systems, the inventive methods and systems disclosed herein include several properties that make it scalable and applicable to a production environment. The inventive concepts disclosed herein are at least deterministic, computationally efficient, run in constant time and can be deployed in many common non-holonomic systems.

    Systems and methods for precise navigation of autonomous devices
    19.
    发明授权
    Systems and methods for precise navigation of autonomous devices 有权

    公开(公告)号：US11099575B2

    公开(公告)日：2021-08-24

    申请号：US16260590

    申请日：2019-01-29

    Applicant: Brain Corporation

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

    IPC: G05D1/02

    Abstract: The safe operation and navigation of robots is an active research topic for many real-world applications, such as the automation of large industrial equipment. This technological field often requires heavy machines with arbitrary shapes to navigate very close to obstacles, a challenging and largely unsolved problem. To address this issue, a new planning architecture is developed that allows wheeled vehicles to navigate safely and without human supervision in cluttered environments. The inventive methods and systems disclosed herein belong to the Model Predictive Control (MPC) family of local planning algorithms. The technological features disclosed herein works in the space of two-dimensional (2D) occupancy grids and plans in motor command space using a black box forward model for state inference. Compared to the conventional methods and systems, the inventive methods and systems disclosed herein include several properties that make it scalable and applicable to a production environment. The inventive concepts disclosed herein are at least deterministic, computationally efficient, run in constant time and can be deployed in many common non-holonomic systems.

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