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

• URL: http://arxiv.org/abs/2001.02330v1
• Date: Wed, 8 Jan 2020 01:14:11 GMT
• Title: High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
• Authors: Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang, Qinru Qiu
• Abstract summary: We develop 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.
• Score: 6.47137925955334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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