计算机科学
反向动力学
过程(计算)
运动学
控制器(灌溉)
任务(项目管理)
人工智能
控制工程
机器人
程序设计语言
工程类
物理
系统工程
经典力学
农学
生物
作者
Yuqiang Yang,Darong Huang,Chen Chen,Chao Zeng,Yong He,Chenguang Yang
标识
DOI:10.1109/smc53992.2023.10394442
摘要
This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github † † https://github.com/yuqiang-yang/SMC2023-Video.
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