Learning from demonstration for semi-autonomous teleoperation
Learning from demonstration for semi-autonomous teleoperation
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DOI：
10.1007/s10514-018-9745-2
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发表时间：
2019-03-01
影响因子：
3.5
通讯作者：
Calinon, Sylvain
中科院分区：
文献类型：
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作者：
Havoutis, Ioannis;Calinon, Sylvain
在深海或太空等领域的遥操作通常需要完成一系列重复性任务。我们提出了一个框架，使用概率的方法来学习从示范模型的操纵任务。我们展示了这样一个框架可以用于在一个水下机器人遥操作的上下文中，以协助操作员。学习的表示可以用于解决操作员和机器人的空间之间的不一致，在结构化的方式，并作为一个回退系统，以执行以前学习的任务时，自动遥控操作是不可能的。我们评估我们的框架与现实的远程操作模拟任务与一组志愿者，显示了显着减少的时间来完成任务时，我们的方法被使用。此外，我们说明了系统如何可以执行以前学习的任务时，与操作员的通信丢失自主。
Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework can be used in an underwater ROV teleoperation context to assist the operator. The learned representation can be used to resolve inconsistencies between the operator's and the robot's space in a structured manner, and as a fall-back system to perform previously learned tasks autonomously when teleoperation is not possible. We evaluate our framework with a realistic ROV task on a teleoperation mock-up with a group of volunteers, showing a significant decrease in time to complete the task when our approach is used. In addition, we illustrate how the system can execute previously learned tasks autonomously when the communication with the operator is lost.