.png){kind=icon}
返回
Optimised Learning from Demonstrations for Collaborative Robots
.png){kind=icon}
OA
AI
DOI：10.1016/j.rcim.2021.102169.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The approach of Learning from Demonstrations (LfD) can support human operators especially those without much programming experience to control a collaborative robot (cobot) in an intuitive and convenient means. Gaussian Mixture Model and Gaussian Mixture Regression (GMM and GMR) are useful tools for implementing such a LfD approach. However, well-performed GMM/GMR require a series of demonstrations without trembling and jerky features, which are challenging to achieve in actual environments. To address this issue, this paper presents a novel optimised approach to improve Gaussian clusters then further GMM/GMR so that LfD enabled cobots can carry out a variety of complex manufacturing tasks effectively. This research has three distinguishing innovative characteristics: 1) a Gaussian noise strategy is designed to scatter demonstrations with trembling and jerky features to better support the optimisation of GMM/GMR; 2) a Simulated Annealing-Reinforcement Learning (SA-RL) based optimisation algorithm is developed to refine the number of Gaussian clusters in eliminating potential under-/over-fitting issues on GMM/GMR; 3) a B-spline based cut-in algorithm is integrated with GMR to improve the adaptability of reproduced solutions for dynamic manufacturing tasks. To verify the approach, cases studies of pick-and-place tasks with different complexities were conducted. Experimental results and comparative analyses showed that this developed approach exhibited good performances in terms of computational efficiency, solution quality and adaptability.
Keyword:
Learn i n g from Demonstration s
Gaussian Mi x ture Model
Collaborative Robots
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11.4
论文数:
3.3K
被引数:
1.3W
机构
引用论文
.png){kind=icon}
分享收藏A simulated annealing-based optimization approach for integrated process planning and scheduling基于模拟退火的集成过程计划和调度优化方法
OAAI
OAAI分享收藏
查看更多内容