首页> 中文期刊> 《中国电子杂志(英文版)》 >Generating Basic Unit Movements with Conditional Generative Adversarial Networks

Generating Basic Unit Movements with Conditional Generative Adversarial Networks

         

摘要

Arm motion control is fundamental for robot accomplishing complicated manipulation tasks.Different movements can be organized by configuring a series of motion units. Our work aims at equipping the robot with the ability to carry out Basic unit movements(BUMs), which are used to constitute various motion sequences so that the robot can drive its hand to a desired position. With the definition of BUMs, we explore a learning approach for the robot to develop such an ability by leveraging deep learning technique. In order to generate the BUM regarding to the current arm state,an internal inverse model is developed. We propose to use Conditional generative adversarial networks(CGANs) to establish the inverse model to generate the BUMs. The experimental results on a humanoid robot PKU-HR6.0Ⅱ illustrate that CGANs could successfully generate multiple solutions given a BUM, and these BUMs can be used to constitute further reaching movement effectively.

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号