Understanding spatial and visual information is essential for a navigation agent who follows natural language instructions. The current Transformer-based VLN agents entangle the orientation and vision information, which limits the gain from the learning of each information source. In this paper, we design a neural agent with explicit Orientation and Vision modules. Those modules learn to ground spatial information and landmark mentions in the instructions to the visual environment more effectively. To strengthen the spatial reasoning and visual perception of the agent, we design specific pre-training tasks to feed and better utilize the corresponding modules in our final navigation model. We evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and achieve the state of the art results on both benchmarks.
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This content will become publicly available on July 1, 2024
VLN-Trans, Translator for the Vision and Language Navigation Agent
Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the
different vision abilities of the instructor and the modeled agent. 2. The mentioned landmarks are applicable to multiple targets, thus not distinctive for selecting the target among
the candidate viewpoints. To deal with these issues, we design a translator module for the
navigation agent to convert the original instructions into easy-to-follow sub-instruction
representations at each step. The translator needs to focus on the recognizable and distinctive landmarks based on the agent’s visual abilities and the observed visual environment. To achieve this goal, we create a new synthetic sub-instruction dataset and design specific tasks to train the translator and the navigation agent. We evaluate our approach on Room2Room (R2R), Room4room (R4R), and Room2Room Last (R2R-Last) datasets and achieve state-of-the-art results on multiple benchmarks.
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- Award ID(s):
- 2028626
- NSF-PAR ID:
- 10419588
- Date Published:
- Journal Name:
- The 61st Annual Meeting of the Association for Computational Linguistics (ACL-2023)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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