首页> 外国专利> SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS ON A CLOUD SERVER USING SENSORY DATA COLLECTED BY ROBOTS

SYSTEMS AND METHODS FOR TRAINING NEURAL NETWORKS ON A CLOUD SERVER USING SENSORY DATA COLLECTED BY ROBOTS

机译:使用机器人收集的感官数据培训云服务器上神经网络的系统和方法

摘要

Systems and methods for training neural networks on a cloud server using sensory data collected by plurality of robots. According to at least one non-limiting exemplary embodiment, a system for training a model is disclosed. The model may be derived from one or more trained neural networks, the neural networks being trained using data collected by one or more robots. Advantageously, data collection by robots may enhance consistency, reliability, and quality of data received for use in training one or more neural networks. The model may be utilized by robots, upon sufficient training of the neural networks, such that the robots may identify features within their environments. Advantageously, the model may be trained on a cloud server and utilized by individual robots for use in enhancing autonomy of the robots, wherein the utilization of the model requires significantly fewer computational resources than training of the neural networks to develop the model.
机译:使用由多个机器人收集的感官数据训练云服务器上神经网络的系统和方法。根据至少一个非限制性示例性实施例,公开了一种用于训练模型的系统。该模型可以源自一个或多个训练的神经网络,使用由一个或多个机器人收集的数据训练的神经网络。有利地,机器人的数据收集可以增强所接收的数据的一致性,可靠性和质量,以用于训练一个或多个神经网络。在充分的神经网络训练时,机器人可以使用该模型,使得机器人可以识别其环境中的特征。有利地,该模型可以训练在云服务器上,并且由各个机器人使用,以增强机器人的自主权,其中模型的利用率比神经网络的训练显着更少,以开发模型。

著录项

  • 公开/公告号WO2021097426A1

    专利类型

  • 公开/公告日2021-05-20

    原文格式PDF

  • 申请/专利权人 BRAIN CORPORATION;

    申请/专利号WO2020US60731

  • 发明设计人 SZATMARY BOTOND;ROSS DAVID;

    申请日2020-11-16

  • 分类号G06N20;G06N3/04;G06N3/08;

  • 国家 US

  • 入库时间 2022-08-24 18:51:12

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