Check the preview of 2nd version of this platform
being developed by the open MLCommons taskforce on automation and reproducibility
as a free, open-source and technology-agnostic on-prem platform.
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
lib:3873b45ed9a8e7cd (v1.0.0)
Authors: Amar Shrestha,Krittaphat Pugdeethosapol,Haowen Fang,Qinru Qiu
ArXiv: 2001.02330
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/2001.02330v1
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.
ArXiv: 2001.02330
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/2001.02330v1
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.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)