# VORL-EXPLORE: A Hybrid Learning Planning Approach for Multi-Robot Exploration in Dynamic Environments

- Source: arXiv cs.RO
- Published: 2026-10-26T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/f14igiri2qus9f50tt7ae2o53
- Original: https://arxiv.org/abs/2603.07973

## Summary

The authors propose VORL-EXPLORE, a hybrid learning and planning framework that uses execution fidelity to estimate local navigability and execution reliability, coupling it into a Voronoi allocation objective with inter-robot repulsion and arbitrating during navigation between global A* guidance and a reactive reinforcement learning policy. The fidelity model is recalibrated using self-supervised pseudo-labels generated from recent progress and safety outcomes. Experiments on randomized dynamic grids and an ablation under severe congestion show higher success rates, shorter exploration paths, and lower inter-robot coverage overlap, along with a deployment example in a Gazebo factory scenario; the code is open-sourced.