Time-Aware Assistive Navigation

• URL: http://arxiv.org/abs/2609.05596v1
• Date: Fri, 04 Sep 2026 17:59:47 GMT
• Title: Time-Aware Assistive Navigation
• Abstract summary: Current language models rarely plan over whether and when to realize a real-time response to a user.<n>We introduce a large-scale benchmark for an egocentric, assistive navigation task in complex outdoor environments.<n>We show that a simple yet effective modification of the model, including direct supervision to predict the underlying reason for each instruction, yields significant performance gains.
• Score: 17.665122992601788
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Can interactive vision-and-language agents learn not just what to say but also \textbf{\textit{when}} to say it? Current language models rarely plan over whether and when to realize a real-time response to a user. However, providing accurate and timely support for human decision-making, such as when guiding visually impaired individuals through urban environments, requires careful real-time responsiveness--poorly timed responses can distract users or add unnecessary cognitive load. As a machine intelligence challenge for Multimodal Large Language Model (MLLM)-based agents, we introduce a large-scale multimodal benchmark for an egocentric, assistive navigation task in complex outdoor environments. Using this benchmark, we uncover a fundamental limitation of off-the-shelf MLLMs in delivering safe and time-sensitive navigation instructions, even with model fine-tuning on substantial amounts of data. We then demonstrate that a simple yet effective modification of the model, including direct supervision to predict the underlying reason for each instruction, yields significant performance gains across open-loop, closed-loop, and sim-to-real generalization settings. However, our analysis highlights persistent challenges in temporal reasoning, safety-critical object awareness, and relational and distance understanding. To advance the development of scalable assistive agents, we will release our simulation, benchmark, and code (available at the project website: https://timeli-icra.github.io/).

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