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Preemptive Instruction Following with Resumption
Zerun Liu, Lechao Cheng, Yimin Liu, Shengeng Tang, Yaxiong Wang, Zhun Zhong

Preemptive Instruction Following with Resumption

Computer Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

Long-horizon embodied navigation may be interrupted by a higher-priority instruction before the current task sequence is complete. The agent must finish its current atomic task, execute the inserted task, and resume the remaining sequence from the updated environment state, where its viewpoint and available navigation candidates may have changed. To evaluate this capability, we introduce PIFR benchmark, a novel task for runtime task preemption and resumption in embodied navigation. PIFR includes single, repeated, and nested interruption settings,with stage-specific metrics for inserted-task completion, resumption of the original task, and completion of the full sequence. Evaluations show that recovery performance varies across interruption conditions, while aggregate success can obscure differences in interruption handling and resumption. We further propose a lightweight candidate-ranking baseline to improve target re-grounding after interruption. Together, PIFR and the baseline provide a systematic framework for studying interruption handling and task recovery in long-horizon embodied navigation.

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Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Preemptive Instruction Following with Resumption
Author / Organization Zerun Liu, Lechao Cheng, Yimin Liu, Shengeng Tang, Yaxiong Wang, Zhun Zhong
Subject & Discipline cs.RO
Categories Computer Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-877
Original Source Link Visit Source Page on arxiv.org »