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Robots with Brain-Like Information Processing

具有类脑信息处理能力的机器人

基本信息

批准号：
10450095
负责人：
YOSHIMOTO Ken-ichi
金额：
$ 7.81万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 1999
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10450095/
关键词：
Motion primitive Brain-like information processing Short term and long term memory Mimesis Probabilistic neural network Hidden Markov model ヒューマノイドセンサー行動発現非線形力学

项目摘要

The main results of this research are summarized as in the following four paragraphs :(1) Development of a Multi-Sensored Torso RobotA torso robot with dual 7 DOF manipulators with a three-fingered hand each was fabricated and equipped with multi-sensory devices. Namely, six of three-axes finger-tip force sensors, a six-axes wrist force/torque Sensor, joint angle Sensors, and dual CCD cameras whose images are sent to two different image processing systems.(2) Grasp With Reactive Behaviors' NetworkA learning algorithm was developed hr the internal parameters of the reactive behavior's network. The primitive reactive behaviors were designed and programmed using the torso robot and the multi-Sensory system. The algorithm was then installed into the torso robot and applied to the reactive grasp.(3) Brain-Like Information Processing System with the Dual-Storage Memory ModelThe associative memory model with the long-term and short-term storage was designed using neural networks and implemented for the torso model. In the experiments, ten sequential placements of four colored blocks were shown and taught to the robot every three seconds while the internal rehearsal was repeated to strengthen the memory. The robot playbacked the sequence with the arm and fingers based on the associative memory, which indicated the features of human memory.(4) Understanding the Others with the Self Motion PrimitivesIt is believed ill the brain science that the mimesis is the basement of symbol manipulation, language, and logical thinking of the human and the large primates. We investigated the implementation of mimesis using the motion primitives of humanoid robots. The probabilistic neural networks and the hidden Markov model were devised for the integration. The computer simulation of the whole body motion were executed and showed the fundamental effectiveness.

本文的主要研究成果概括为以下四个部分：（1）多传感器躯干机器人的研制了一种具有两个7自由度操作手的躯干机器人，每个操作手具有一个三指手，并配备了多传感器装置。即，六个三轴指尖力传感器、六轴腕力/扭矩传感器、关节角度传感器和双CCD相机，其图像被发送到两个不同的图像处理系统。(2)对反应行为网络的学习提出了一种基于反应行为网络内部参数的学习算法。使用躯干机器人和多感觉系统设计和编程了原始反应行为。然后将该算法安装到躯干机器人中并应用于反应式抓取。(3)基于双存储记忆模型的类脑信息处理系统利用神经网络设计了具有长、短期存储的联想记忆模型，并对躯干模型进行了实现。在实验中，四个彩色块的十个顺序放置被展示，并每三秒教机器人一次，同时重复内部排练以加强记忆。该机器人利用联想记忆的方法，利用手臂和手指的动作回放序列，体现了人类记忆的特点。(4)脑科学认为，模仿是人类和大型灵长类动物进行符号操作、语言和逻辑思维的基础。我们研究了使用人形机器人的运动基元进行模仿的实现。概率神经网络和隐马尔可夫模型的集成设计。通过计算机仿真，验证了该方法的有效性.