Novel learning from demonstration approach for repetitive teleoperation tasks
Novel learning from demonstration approach for repetitive teleoperation tasks
复制标题
DOI：
10.1109/whc.2017.7989877
复制
发表时间：
2017-06
期刊：
影响因子：
--
通讯作者：
Affan Pervez;Arslan Ali;J. Ryu;Dongheui Lee
中科院分区：
文献类型：
--
作者：
Affan Pervez;Arslan Ali;J. Ryu;Dongheui Lee
虽然遥操作为机器人提供了在极端条件下代替人类操作的可能性，但遥操作机器人仍然需要人类操作员繁重的脑力工作。从演示中学习可以通过学习重复的遥操作任务来减轻人类操作员的负担。然而，具有挑战性的问题之一是，通过遥操作的演示是不太一致的相比，其他形式的人类演示。为了解决这个问题，我们提出了一种基于动态运动基元（DMPs）的学习方案，它可以处理不一致，不完整和不完整的演示。特别是，我们提出了一个新的期望最大化（EM）算法，它可以同步和编码演示的时间和空间的差异，不同的初始和最终条件和部分执行。在3自由度主从遥操作系统上进行了3个不同的钉孔任务实验，对所提出的算法进行了测试和验证。
While teleoperation provides a possibility for a robot to operate at extreme conditions instead of a human, teleoperating a robot still demands a heavy mental workload from a human operator. Learning from demonstrations can reduce the human operator's burden by learning repetitive teleoperation tasks. However, one of challenging issues is that demonstrations via teleoperation are less consistent compared to other modalities of human demonstrations. In order to solve this problem, we propose a learning scheme based on Dynamic Movement Primitives (DMPs) which can handle less consistent, asynchronized and incomplete demonstrations. In particular we proposed a new Expectation Maximization (EM) algorithm which can synchronize and encode demonstrations with temporal and spatial variances, different initial and final conditions and partial executions. The proposed algorithm is tested and validated with three different experiments of a peg-in-hole task conducted on 3-Degree of freedom (DOF) masterslave teleoperation system.