DMP and GMR based teaching by demonstration for a KUKA LBR robot
DMP and GMR based teaching by demonstration for a KUKA LBR robot
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DOI：
10.23919/iconac.2017.8081982
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alexander Hewitt;Chenguang Yang;Yong Li;Rongxin Cui
中科院分区：
文献类型：
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作者：
Alexander Hewitt;Chenguang Yang;Yong Li;Rongxin Cui
本文研究了KUKA轻型机器人（LBR）的示教问题。运动由人类操作员记录，然后数据用于对非线性系统建模，即，动态运动基元（dynamic motor primitives，缩写为MOT）。为了从多个演示中学习，采用高斯混合模型（GMM），而不是使用传统的高斯过程来评估非线性项。在此基础上，采用高斯混合回归（GMR）算法在三维空间中生成具有较小位置误差的合成轨迹。所提出的方法进行了测试，并通过执行两个任务与KUKA iiwa机器人证明。
This paper investigates the problem of Teaching by Demonstration (TbD) on a KUKA lightweight robot (LBR). Motions are recorded by a human operator, and then the data is used to model a nonlinear system, i.e., the dynamic motor primitive (DMP). In order to learn from multiple demonstrations, Gaussian Mixture Models (GMM) are employed rather than using conventional Gaussian process for the evaluation of the non-linear term of the DMP. Then the Gaussian mixture regression (GMR) algorithm is applied to generate a synthesized trajectory with smaller position errors in 3D space. The proposed approach is tested and demonstrated by performing two tasks with KUKA iiwa robot.