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Preprint · arXiv · October 17, 2026

Escape Without a Recipe: Maneuver-Agnostic Mars Rover Recovery from Granular Entrapment

Meraj Hossain Promit, Chandak Chakma, Md Jubair Ahmed Sourov, Sejuti Rahman

Sejuti Rahman
Read on arXivPDFDOI 10.48550/arxiv.2610.24827

Abstract

Wheeled planetary rovers immobilised in loose regolith have no principled recovery controller: scripted maneuvers are terrain-specific, and naive driving deepens entrapment. We introduce a recurrent policy that learns to escape without a predefined maneuver. The framework rests on a verified trap: in a continuum material point method (MPM) simulator we identify a configuration at sinkage-to-radius ratio z/r = 2.0 where the strongest of ten scripted controllers succeeds in only 7/32 trials, yet the regolith retains traction and continues to shear, confirming granular entrapment rather than a numerical artifact. A frozen entrapment detector, trained on 81,771 windows spanning three materials and six sinkage levels, supplies a three-state belief to the policy observation. A separate simulator mobility latch gates training rewards. Across five training seeds, mean action evaluation achieves 62.5% escape (range 28.1-81.3%; 32 trials per seed) versus 7/32 for the strongest scripted controller and 0/32 for action noise. A numerical audit identifies a coupling-buffer fault that silently discarded 9.7% of contact nodes at 16 parallel environments. Results establish recovery in the tested simulation regime; physical transfer remains unverified.

Soil Mechanics and Vehicle DynamicsReinforcement Learning in RoboticsRobotics and Sensor-Based Localizationdeep reinforcement learning

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