Papers with Navigation

3 papers
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning (2024.naacl-long)

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Challenge: Towards Byzantine-robust federated embodied agent learning, we study the attack and defense for the task of vision-and-language navigation (VLN)
Approach: They propose a new method to defend against a navigation-and-language navigation attack using navigation as wish (NAW) the method provides the server with a 'prompt' of the vision-and language alignment variance between benign and malicious clients so they can be distinguished during training.
Outcome: The proposed method outperforms other state-of-the-art defense methods on two VLN datasets.
SCAN: Structured Capability Assessment and Navigation for LLMs (2026.acl-long)

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Challenge: Existing research has focused on approximating model rankings, but such benchmarks fail to provide users and developers with a comprehensive and fine-grained understanding of a specific model’s capabilities.
Approach: They propose a framework that enables detailed characterization of LLM capabilities through comprehensive and fine-grained evaluation.
Outcome: The proposed framework enables detailed characterization of large language models through comprehensive and fine-grained evaluation.
NDH-Full: Learning and Evaluating Navigational Agents on Full-Length Dialogue (2021.emnlp-main)

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Challenge: Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent’s ability to interact with humans for assistance and navigate based on natural language responses.
Approach: They propose a vision-and-dialogue navigation task which evaluates the agent's ability to interact with humans and navigate based on natural language responses.
Outcome: The proposed model performs well on the Navigation from Dialogue History task, but it is not evaluated by the primary metric Goal Progress.

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