Beyond Fixed Goal Delivery: Online POMDP Planning for Target Interception in Crowds
This paper proposes an online Partially Observable Markov Decision Process (POMDP) planning method for intercepting moving targets in crowded environments. Using tree search under a fixed computational budget, it compares a sequential path-speed planner and a unified steering-speed planner. Simulations with up to 200 humans show that at high crowd density, the unified planner achieves a 31 percentage point higher safe-interception rate and requires 44% less time, revealing a structural limitation of spatial restriction in sequential planning.
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[Submitted on 20 Jul 2026]
Title:Beyond Fixed Goal Delivery: Online POMDP Planning for Target Interception in Crowds
View a PDF of the paper titled Beyond Fixed Goal Delivery: Online POMDP Planning for Target Interception in Crowds, by Himanshu Gupta and 4 other authors
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Abstract:Target interception in crowded environments requires reaching a moving objective while navigating among multiple uncertain human agents. Since human navigation intent is not directly observable, the robot must reason over multiple possible future interaction outcomes. We formulate interception in crowds as a partially observable Markov decision process and solve it online using tree search under a fixed computational budget. In this setting, the action-space structure directly shapes the search tree and how computational effort is allocated. We perform a controlled comparison between a sequential path-speed planner, which first plans a spatial path and then modulates speed along it, and a unified planner that jointly branches over steering and speed within tree search. Across simulations with up to 200 humans, both approaches perform similarly at low crowd density but diverge sharply as density increases. At the highest crowd density, the sequential planner has a safe-interception rate 31 percentage points lower and requires 44% more time than the unified steering-speed planner, revealing a structural limitation of spatial restriction. Project webpage: this https URL
Comments: Accepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.18517 [cs.RO]
(or arXiv:2607.18517v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18517
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Himanshu Gupta [view email] [v1] Mon, 20 Jul 2026 21:18:01 UTC (12,425 KB)
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