论文标题
倾向于学习自主地面机器人导航任务
Towards Preference Learning for Autonomous Ground Robot Navigation Tasks
论文作者
论文摘要
我们对自主机器人行为的设计感兴趣，这些自动机器人行为学习用户而不是继续交互的偏好，目的是以用户期望的方式有效地执行导航行为。在本文中，我们正在讨论我们正在进行的工作，以使用基于首选项的强化学习在探索任务中修改机器人导航行为的通用模型。这种方法的新颖贡献是，它结合了强化学习，运动计划和自然语言处理，以使自主代理可以通过与人类队友的持续对话来学习，而不是一次性指示。
We are interested in the design of autonomous robot behaviors that learn the preferences of users over continued interactions, with the goal of efficiently executing navigation behaviors in a way that the user expects. In this paper, we discuss our work in progress to modify a general model for robot navigation behaviors in an exploration task on a per-user basis using preference-based reinforcement learning. The novel contribution of this approach is that it combines reinforcement learning, motion planning, and natural language processing to allow an autonomous agent to learn from sustained dialogue with a human teammate as opposed to one-off instructions.
