Motion learning in variable environments using probabilistic flow tubes
Motion learning in variable environments using probabilistic flow tubes
复制标题
使用概率流管在可变环境中进行运动学习
DOI：
10.1109/icra.2011.5980530
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
B. Williams
中科院分区：
文献类型：
--
作者：
Shuonan Dong;B. Williams
对于具有许多自由度的系统来说，通过低水平的复杂运动来指挥自主系统可能是乏味的或不切实际的。允许操作者直接演示所需的运动通常可以实现更直观和有效的交互。从演示中学习的领域中的两个挑战包括（1）如何最好地表示所学习的动作以准确地反映人的意图，以及（2）如何使所学习的动作能够容易地应用于新的情况。本文介绍了一种新的连续动作表示称为概率流管，可以在执行过程中提供灵活性，同时鲁棒地编码人类的预期运动。我们的方法还自动确定某些定性特征的运动，使这些特征可以被保存时，自主执行的运动在一个新的情况。我们证明了我们的运动学习方法的有效性，在模拟的二维环境中，并在全地形六肢外星人探险家（ATHALGOT）机器人执行对象操作任务。
Commanding an autonomous system through complex motions at a low level can be tedious or impractical for systems with many degrees of freedom. Allowing an operator to demonstrate the desired motions directly can often enable more intuitive and efficient interaction. Two challenges in the field of learning from demonstration include (1) how to best represent learned motions to accurately reflect a human's intentions, and (2) how to enable learned motions to be easily applicable in new situations. This paper introduces a novel representation of continuous actions called probabilistic flow tubes that can provide flexibility during execution while robustly encoding a human's intended motions. Our approach also automatically determines certain qualitative characteristics of a motion so that these characteristics can be preserved when autonomously executing the motion in a new situation. We demonstrate the effectiveness of our motion learning approach both in a simulated two-dimensional environment and on the All-Terrain Hex-Limbed Extra-Terrestrial Explorer (ATHLETE) robot performing object manipulation tasks.