MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
Overview
This paper introduces MineXplore, an open-source MuJoCo-based navigation benchmark designed for autonomous robot exploration in GPS-denied underground environments.
Generated from attributed reports · Updated 46 minutes ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“arXiv:2606.04569v2 Announce Type: replace Abstract: Underground mines present extreme conditions for autonomous robot navigation: GPS is denied, lighting is degraded, and tunnel topology is loop-rich and non-convex. Simulation benchmarks grounded in real production-mine geometry and compatible with GPU-accelerated learning pipelines do not yet exist in the open-source ecosystem. We present MineXplore, an open-source”
Exact source · revision 1Source owner not reported
Reported quantity · IoU: 0.9538 other · Basis not reported
“ntersection over Union (IoU) of 0.9538 against the source survey map, and surface texture similarity scores 79.4% across six structural dimensions. A single-agent PPO baseline trained via RLlib across five independent random seeds achieves a best rolling coverage of 88.89% (3 of 5 seeds reaching the 90% coverage target), confirming that MineXplore supports stable and reproducible policy learning under realistic under”
Exact source · revision 1Source owner not reported
Reported quantity · tunnel network area: 104423 other · Basis not reported
“MuJoCo-based navigation benchmark derived from the Leung et al. 2017 Chilean underground copper mine dataset. The environment reconstructs a 104,423 sq.m tunnel network through an six-stage contour-to-MJCF pipeline incorporating octagonal wall cross-sections, LiDAR-sourced jagged wall geometry, three terrain friction zones, a global 5 degree incline, and periodic spot lighting. Geometric fidelity is validated at an I”
Exact source · revision 1Source owner not reported
Reported quantity · incline angle: 5 other · Basis not reported
“MuJoCo-based navigation benchmark derived from the Leung et al. 2017 Chilean underground copper mine dataset. The environment reconstructs a 104,423 sq.m tunnel network through an six-stage contour-to-MJCF pipeline incorporating octagonal wall cross-sections, LiDAR-sourced jagged wall geometry, three terrain friction zones, a global 5 degree incline, and periodic spot lighting. Geometric fidelity is validated at an I”
Exact source · revision 1Source owner not reported
Reported quantity · structural dimensions: 6 other · Basis not reported
“ntersection over Union (IoU) of 0.9538 against the source survey map, and surface texture similarity scores 79.4% across six structural dimensions. A single-agent PPO baseline trained via RLlib across five independent random seeds achieves a best rolling coverage of 88.89% (3 of 5 seeds reaching the 90% coverage target), confirming that MineXplore supports stable and reproducible policy learning under realistic under”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
This paper introduces MineXplore, an open-source MuJoCo-based navigation benchmark designed for autonomous robot exploration in GPS-denied underground environments.
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