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A containerized simulation platform for robot learning peg-in-hole task
Liu NL(刘乃龙)1,2; Liu ZM(刘钊铭)1,2; Wei Q(魏青)1,2; Cui L(崔龙)1
作者部门机器人学研究室
会议名称13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
会议日期May 31 - June 2, 2018
会议地点Wuhan, China
会议录名称Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
出版者IEEE
出版地New York
2018
页码1290-1295
收录类别EI
EI收录号20183005587597
产权排序1
ISBN号978-1-5386-3757-9
关键词Robotics remote operation artificial intelligence machine learning peg-in-hole
摘要In this paper, we build a simulation platform for robot learning peg-in-hole(PiH) task for to study the strategy of inserting the pegs with different geometry features in PiH task with tele-operation. PiH task as a typical assembly task in the industrial field has been widely researched. Though many researches proposed some general solution for PiH, most of them only rely on accurate force control can be achieved or the environment is structured. In the unstructured environment, it is still a huge challenge. And different sizes and shapes of pegs will significantly increase the difficulty of operation even human-in-loop method because of force and torque introduced from the contact environment and uncertainty from vision, many previous strategies cannot be adapted to these situations. Recently, machine learning method has been achieved many successful applications on robotics which can adapt on different situations with many uncertainties, but making robots learning in the real world still needs more setup, and it also may destroy the robots. Our simulation platform which based on state of art ROS and Gazebo and shipped with Docker and Weave virtual network provides a reproducible and easily deployable platform for robot learning the PiH task. And we also include a tele-operation method for the human operator to tele-operate the simulation robot with force feedback during the peg is approaching to the hole which will enable robot learning trajectory execution from human demonstrations.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22296
专题机器人学研究室
通讯作者Liu NL(刘乃龙)
作者单位1.State Key Laboratory of Robotics Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS) Shenyang, Liaoning Province, China
2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Liu NL,Liu ZM,Wei Q,et al. A containerized simulation platform for robot learning peg-in-hole task[C]. New York:IEEE,2018:1290-1295.
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