# High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET

arXiv: https://arxiv.org/abs/2001.02330 · HTML: https://mlsanity.com/paper/2001.02330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Ground then Navigate: Language-guided Navigation in Dynamic Scenes](https://mlsanity.com/paper/2209.11972/) (2022)
- [NSP: A Neuro-Symbolic Natural Language Navigational Planner](https://mlsanity.com/paper/2409.06859/) (2024)

## Abstract (arXiv)

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.