MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment

Badrikanath Praharaj, Sreeram M.V, ABHISHEK S

Keywords: simulation environment, underground mining robotics, reinforcement learning, MuJoCo, navigation bench- mark, exploration

TL;DR: Compiled real production-mine survey data into an open-source MuJoCo benchmark, bridging GPS-denied underground topology with GPU-accelerated RL pipelines for the RL & RAS community.

Abstract:

Underground mines present extreme conditions for
autonomous robot navigation: GPS is denied, lighting is de-
graded, and tunnel topology is loop-rich and non-convex. Simula-
tion 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 MuJoCo-based navigation benchmark derived from
the Leung et al. 2017 Chilean underground copper mine dataset. The environment reconstructs a 104,423 m2 tunnel network
through an six-stage contour-to-MJCF pipeline incorporating oc-
tagonal wall cross-sections, LiDAR-sourced jagged wall geometry,
three terrain friction zones, a global 5◦ incline, and periodic
spot lighting. Geometric fidelity is validated at an Intersection
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% ± 1.74% (3 of 5 seeds reaching the 90% coverage target),
confirming that MineXplore supports stable and reproducible
policy learning under realistic underground sensing and topology.

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Submission Number: 6