论文标题
具有自然语言方向和R-NET的行为机器人导航的高级计划
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
论文作者
论文摘要
当已知导航环境时，可以将其表示为图表，其中地标是节点，从节点到节点的机器人行为是边缘，并且该路由是一组行为指令。从源到目标的路径路径可以视为一类组合优化问题，其中该路径是一组离散项目的顺序子集。指针网络是一个基于注意力的重复网络，适用于此类任务。在本文中，我们利用了带有封闭的注意力的修改后的R-NET和自我匹配的注意力，将自然语言指令转化为行为机器人导航的高级计划，通过对行为导航图的理解来使指针网络能够产生一系列代表路径的行为。导航图数据集上的测试表明，我们的模型在已知和未知环境的最新方法上都优于最先进的方法。
When the navigational environment is known, it can be represented as a graph where landmarks are nodes, the robot behaviors that move from node to node are edges, and the route is a set of behavioral instructions. The route path from source to destination can be viewed as a class of combinatorial optimization problems where the path is a sequential subset from a set of discrete items. The pointer network is an attention-based recurrent network that is suitable for such a task. In this paper, we utilize a modified R-NET with gated attention and self-matching attention translating natural language instructions to a high-level plan for behavioral robot navigation by developing an understanding of the behavioral navigational graph to enable the pointer network to produce a sequence of behaviors representing the path. Tests on the navigation graph dataset show that our model outperforms the state-of-the-art approach for both known and unknown environments.
