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。

公开(公告)号：US20150127150A1

公开(公告)日：2015-05-07

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: B25J9/16

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.

Abstract translation: 机器人设备可以由训练者训练，该训练者使用与机器人的物理接触沿着目标轨迹引导机器人。 机器人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。 训练者可以观察机器人执行任务。 响应于观察目标行为与实际行为之间的差异，培训者可以通过触觉动作提供教学输入。 机器人可以基于由机器人的学习过程产生的内部控制信号与训练输入的组合来执行动作。 机器人可以基于预测状态与机器人的实际状态的比较来推断教学输入。 机器人的学习过程可以根据教学输入进行调整，以减少后续试验期间的差异。

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

IPC: G06F15/18 ,  B25J9/16 ,  G05B13/02 ,  G06N3/02 ,  G06N99/00

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。

公开(公告)号：US20140379624A1

公开(公告)日：2014-12-25

申请号：US13922143

申请日：2013-06-19

Applicant: Brain Corporation

Inventor： FILIP PIEKNIEWSKI ,  Vadim Polonichko ,  Eugene Izhikevich

IPC: G06N3/02

CPC classification number: G06N3/08 ,  G06N3/049

Abstract: Apparatus and methods for processing inputs by one or more neurons of a network. The neuron(s) may generate spikes based on receipt of multiple inputs. Latency of spike generation may be determined based on an input magnitude. Inputs may be scaled using for example a non-linear concave transform. Scaling may increase neuron sensitivity to lower magnitude inputs, thereby improving latency encoding of small amplitude inputs. The transformation function may be configured compatible with existing non-scaling neuron processes and used as a plug-in to existing neuron models. Use of input scaling may allow for an improved network operation and reduce task simulation time.

Abstract translation: 用于由网络的一个或多个神经元处理输入的装置和方法。 基于多个输入的接收，神经元可以产生尖峰。 可以基于输入幅度来确定尖峰生成的延迟。 可以使用例如非线性凹变换来缩放输入。 缩放可以将神经元灵敏度增加到较低幅度的输入，从而改善小振幅输入的延迟编码。 转换函数可以被配置为与现有的非缩放神经元过程兼容，并且用作现有神经元模型的插件。 使用输入缩放可以允许改进的网络操作并减少任务模拟时间。

公开(公告)号：US20240085916A1

公开(公告)日：2024-03-14

申请号：US18379442

申请日：2023-10-12

Applicant: Brain Corporation

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

IPC: G05D1/02 ,  G01C21/20 ,  G01S17/89 ,  G06T7/246 ,  G06V10/74 ,  G06V20/58

CPC classification number: G05D1/0214 ,  G01C21/20 ,  G01S17/89 ,  G06T7/246 ,  G06V10/761 ,  G06V20/58 ,  G05D2201/0211

Abstract: Systems and methods for robotic detection of escalators are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may navigate a learned route and utilize one or more methods of detecting an escalator using data from its sensors. The robot may subsequently avoid the area comprising the escalator.

公开(公告)号：US11331796B2

公开(公告)日：2022-05-17

申请号：US16271234

申请日：2019-02-08

Applicant: Brain Corporation

Inventor： Phil Duffy ,  Jim McCullough ,  Eugene Izhikevich ,  Nicole Renke ,  Justin Couvignou ,  Jimmy Kim

IPC: B25J9/16 ,  B25J15/00 ,  B25J15/04

Abstract: Systems and methods for a universal connection interface between a robot and a plurality of modular attachments are disclosed. The connection interface includes a data connection and a dynamic amplifier configured to adjust output of at least one electromechanically coupled mechanical output; and a processor configured to control gain of the dynamic amplifier.

公开(公告)号：US10728436B2

公开(公告)日：2020-07-28

申请号：US15845891

申请日：2017-12-18

Applicant: Brain Corporation

Inventor： Filip Piekniewski ,  Vadim Polonichko ,  Eugene Izhikevich

IPC: H04N5/225 ,  B60W30/09 ,  B60W30/16 ,  G06K9/00 ,  G02B5/20 ,  G02B13/00 ,  G02B27/00 ,  B60W30/14 ,  G01C3/08 ,  G02B7/38 ,  G06K9/46 ,  G06K9/52 ,  H01L31/0216

Abstract: An optical object detection apparatus and associated methods. The apparatus may comprise a lens (e.g., fixed-focal length wide aperture lens) and an image sensor. The fixed focal length of the lens may correspond to a depth of field area in front of the lens. When an object enters the depth of field area (e.g., sue to a relative motion between the object and the lens) the object representation on the image sensor plane may be in-focus. Objects outside the depth of field area may be out of focus. In-focus representations of objects may be characterized by a greater contrast parameter compared to out of focus representations. One or more images provided by the detection apparatus may be analyzed in order to determine useful information (e.g., an image contrast parameter) of a given image. Based on the image contrast meeting one or more criteria, a detection indication may be produced.

公开(公告)号：US10464213B2

公开(公告)日：2019-11-05

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: G06K9/00 ,  B25J9/16 ,  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：US10391628B2

公开(公告)日：2019-08-27

申请号：US15474880

申请日：2017-03-30

Applicant: Brain Corporation

Inventor： Eugene Izhikevich ,  Dimitry Fisher ,  Jean-Baptiste Passot

IPC: G05G9/00 ,  G05G13/00 ,  B25J9/00 ,  B25J9/10 ,  B25J9/08

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：US20190255703A1

公开(公告)日：2019-08-22

申请号：US16171635

申请日：2018-10-26

Applicant: Brain Corporation

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

IPC: B25J9/16 ,  G06N3/04 ,  G06N3/00

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.