ULN: Towards Underspecified Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2210.10020v1
• Date: Tue, 18 Oct 2022 17:45:06 GMT
• Title: ULN: Towards Underspecified Vision-and-Language Navigation
• Authors: Weixi Feng, Tsu-Jui Fu, Yujie Lu, William Yang Wang
• Abstract summary: Underspecified vision-and-Language Navigation (ULN) is a new setting for vision-and-Language Navigation (VLN) We propose a VLN framework that consists of a classification module, a navigation agent, and an Exploitation-to-Exploration (E2E) module. Our framework is more robust and outperforms the baselines on ULN by 10% relative success rate across all levels.
• Score: 77.81257404252132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions. Despite the significant performance improvement, the wide use of fine-grained instructions fails to characterize more practical linguistic variations in reality. To fill in this gap, we introduce a new setting, namely Underspecified vision-and-Language Navigation (ULN), and associated evaluation datasets. ULN evaluates agents using multi-level underspecified instructions instead of purely fine-grained or coarse-grained, which is a more realistic and general setting. As a primary step toward ULN, we propose a VLN framework that consists of a classification module, a navigation agent, and an Exploitation-to-Exploration (E2E) module. Specifically, we propose to learn Granularity Specific Sub-networks (GSS) for the agent to ground multi-level instructions with minimal additional parameters. Then, our E2E module estimates grounding uncertainty and conducts multi-step lookahead exploration to improve the success rate further. Experimental results show that existing VLN models are still brittle to multi-level language underspecification. Our framework is more robust and outperforms the baselines on ULN by ~10% relative success rate across all levels.

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