Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework
Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework
复制标题
DOI：
10.1109/lra.2019.2896466
复制
发表时间：
2018-10
影响因子：
5.2
通讯作者：
Okan Koç;Jan Peters
中科院分区：
文献类型：
--
作者：
Okan Koç;Jan Peters
从演示中学习是向机器人展示成功行为示例的一种简单直观的方法。然而，人类优化或利用自己的身体而不是机器人的事实（通常称为机器人学中的体现问题）通常会阻止工业机器人以直接的方式执行任务。所示的运动通常没有或不能有效地利用机器人的自由度，而且可能遭受过多的执行错误。在这封信中，我们探讨了解决这些缺点的各种解决方案。特别是，我们从几次成功的乒乓球发球演示中学习稀疏运动原始参数。使用我们的程序学习的参数数量与机器人的自由度无关。此外，可以根据它们在回归任务中的重要性对它们进行排名。学习少量的参数（按顺序排列）是对抗强化学习中维数灾难的理想特征。使用学习到的运动基元在 Barrett WAM 上进行乒乓球发球的真实机器人实验表明，该表示可以用很少的参数成功捕获运动风格。
Learning from demonstrations is an easy and intuitive way to show examples of successful behavior to a robot. However, the fact that humans optimize or take advantage of their body and not of the robot, usually called the embodiment problem in robotics, often prevents industrial robots from executing the task in a straightforward way. The shown movements often do not or cannot utilize the degrees of freedom of the robot efficiently, and moreover can suffer from excessive execution errors. In this letter, we explore a variety of solutions that address these shortcomings. In particular, we learn sparse movement primitive parameters from several demonstrations of a successful table tennis serve. The number of parameters learned using our procedure is independent of the degrees of freedom of the robot. Moreover, they can be ranked according to their importance in the regression task. Learning few parameters, which are ranked, is a desirable feature to combat the curse of dimensionality in reinforcement learning. Real robot experiments on the Barrett WAM for a table tennis serve using the learned movement primitives show that the representation can capture successfully the style of the movement with few parameters.