公开(公告)号：US20150324687A1

公开(公告)日：2015-11-12

申请号：US14468928

申请日：2014-08-26

Applicant: QUALCOMM TECHNOLOGIES INC.

Inventor： Marius Buibas ,  Charles Wheeler SWEET III ,  Mark S. CASKEY ,  Jeffrey Alexander LEVIN

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/04 ,  G06N3/008 ,  G06N3/02 ,  G06N3/049 ,  G06N3/063 ,  G06N3/08

Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

Abstract translation: 具有人工智能和接受训练控制的可扩展机器人装置的装置和方法。 在一个实现中，公开了允许用户完全选择其机器人设备的架构和能力集合的模块化机器人系统。 用户可以添加/删除模块，因为它们各自的功能是必需/免除的。 另外，人工智能是基于神经元网络（例如，刺激神经网络）以及行为控制结构，其允许用户以概念上类似于关于训练驯养动物的模式来训练机器人装置 例如狗或猫（例如，正/负反馈训练范例）。 可训练行为控制结构基于人造神经网络，其模拟活体的脑的神经/突触活动。