Preemptive Instruction Following with Resumption

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.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.35576
Primary Topic
Reinforcement Learning in Robotics
Type
preprint
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preprint

Preemptive Instruction Following with Resumption

Zhun Zhong, Lechao Cheng, Shengeng Tang, Zerun Liu et al.
arXiv (Cornell University)
Reinforcement Learning in Robotics
preprint

Preemptive Instruction Following with Resumption

Zhun Zhong, Lechao Cheng, Shengeng Tang, Zerun Liu, Yaxiong Wang, Yimin Liu
preprint en

Abstract

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.

arXiv (Cornell University)
Reinforcement Learning in Robotics
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Preemptive Instruction Following with Resumption — Zhun Zhong, Lechao Cheng, et al. · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS