aiqbench analysis
This paper applies learning to robot exploration: a fidelity score estimates how navigable and reliable each route is, arbitrates between A* planning and a reinforcement learning policy, and is recalibrated with self-supervised pseudo-labels. The evaluation is limited to randomized simulated grids and a Gazebo scenario, so real-world robustness remains untested. For AI readers, it is a concrete example of combining classical planning with learned reactive policies, with the open code useful for checking the claimed gains in success rate and coverage overlap.
VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
arXiv:2603.07973v2 Announce Type: replace-cross Abstract: Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Without awareness of execution difficulty, allocators may send robots toward frontiers whose routes are congested or unsafe, causing bottlenecks, oscillatory replanning, and redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that makes exploration execution-aware through execution fidelity, an estimate of local navigability and execution reliability. This fidelity score is used to penalize risky or crowded frontiers in a fidelity-coupled Voronoi allocation objective with inter-robot repulsion, and to arbitrate between global $A^*$ guidance and a reactive reinforcement learning policy during navigation. As a result, robots follow efficient global plans in reliable regions and rely more on safer local reactions in crowded or changing areas. The fidelity model is recalibrated using self-supervised pseudo-labels derived from recent progress and safety outcomes. Quantitative experiments on randomized dynamic grids and an ablation under severe traffic show higher success rates, shorter exploration lengths, and lower inter-robot coverage overlap. A Gazebo factory scenario is included as a deployment illustration. The source code is available at https://github.com/21ning/VORL-EXPLORE.
Abstract:Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Without awareness of execution difficulty, allocators may send robots toward frontiers whose routes are congested or unsafe, causing bottlenecks, oscillatory replanning, and redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that makes exploration execution-aware through execution fidelity, an estimate of local navigability and execution reliability. This fidelity score is used to penalize risky or crowded frontiers in a fidelity-coupled Voronoi allocation objective with inter-robot repulsion, and to arbitrate between global $A^*$ guidance and a reactive reinforcement learning policy during navigation. As a result, robots follow efficient global plans in reliable regions and rely more on safer local reactions in crowded or changing areas. The fidelity model is recalibrated using self-supervised pseudo-labels derived from recent progress and safety outcomes. Quantitative experiments on randomized dynamic grids and an ablation under severe traffic show higher success rates, shorter exploration lengths, and lower inter-robot coverage overlap. A Gazebo factory scenario is included as a deployment illustration. The source code is available at this https URL.
Accepted at IROS 2026. 8 pages, 5 figures, 2 tables. Updated manuscript
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2603.07973 [cs.RO]
(or arXiv:2603.07973v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.07973 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 285 words