AI & ML
Preemptive Instruction Following with Resumption
arXiv cs.RO0 views
arXiv:2610.35576v1 Announce Type: new 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.
Source:https://arxiv.org/abs/2610.35576PreviousEnhancing Deep Reinforcement Learning for Robotic Motion Planning via Task DecompositionNextDistributed Predictive Flocking for a Swarm of Miniature Vibration-Driven Robots in a Confined Arena
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