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SPARROW: Survival-POMCP for Adaptive Robot Routing, Observation, and Waiting

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arXiv:2609.21008v1 Announce Type: new Abstract: Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to wait for a blockage to clear, reroute, or acquire more information about the obstacle before acting. We formulate graph navigation among temporary obstacles as a partially observable semi-Markov decision process and introduce SPARROW, a belief-space planner built on Partially Observable Monte Carlo Planning (POMCP). SPARROW searches over traversal, observation, and finite-duration waiting actions while maintaining a particle belief over latent obstacle classes and clearance times. Class-conditioned survival models are learned online from both clearance observations and right-censored encounters where the robot rer…

SourcearXiv RoboticsAuthor: Hshmat Sahak, Aoran Jiao, Nicholas Rhinehart, Timothy D. Barfoot
SPARROW: Survival-POMCP for Adaptive Robot Routing, Observation, and Waiting
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[Submitted on 17 Sep 2026]

Title:SPARROW: Survival-POMCP for Adaptive Robot Routing, Observation, and Waiting

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Abstract:Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to wait for a blockage to clear, reroute, or acquire more information about the obstacle before acting. We formulate graph navigation among temporary obstacles as a partially observable semi-Markov decision process and introduce SPARROW, a belief-space planner built on Partially Observable Monte Carlo Planning (POMCP). SPARROW searches over traversal, observation, and finite-duration waiting actions while maintaining a particle belief over latent obstacle classes and clearance times. Class-conditioned survival models are learned online from both clearance observations and right-censored encounters where the robot reroutes before clearance is observed. A generative model simulates obstacle arrivals and clearances as each action unfolds, so the planner can account for blockages that may occur along alternative routes. We further introduce a value-of-learning criterion that trades the immediate cost of collecting labelled survival data against its expected reduction in future navigation regret. Across two simulation graphs and multiple obstacle-class settings, SPARROW reduces mean time-to-goal by 12-26% relative to OSCAR, a recent survival-based method for the same problem. On a physical mobile robot, SPARROW reduces mean time-to-goal by 20.5% relative to OSCAR while selectively observing, waiting, and rerouting as environment conditions change.

Comments: 8 pages

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2609.21008 [cs.RO]

(or arXiv:2609.21008v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.21008

arXiv-issued DOI via DataCite (pending registration)

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From: Hshmat Sahak [view email] [v1] Thu, 17 Sep 2026 19:01:44 UTC (7,430 KB)

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  • arXiv:2609.21008v1 Announce Type: new Abstract: Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to w…

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