Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media

• URL: http://arxiv.org/abs/2508.11503v2
• Date: Mon, 20 Oct 2025 21:24:24 GMT
• Title: Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media
• Authors: Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez,
• Abstract summary: This work presents a complete sim-to-real framework for developing robust control policies for dynamic waypoint tracking on challenging surfaces.<n>We leverage massively parallel simulation to train reinforcement learning agents across a vast distribution of procedurally generated environments.<n>Our experiments systematically compare multiple reinforcement learning algorithms and action smoothing filters to identify the most effective combinations for real-world deployment.
• Score: 16.948852537273655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable autonomous navigation across the unstructured terrains of distant planetary surfaces is a critical enabler for future space exploration. However, the deployment of learning-based controllers is hindered by the inherent sim-to-real gap, particularly for the complex dynamics of wheel interactions with granular media. This work presents a complete sim-to-real framework for developing and validating robust control policies for dynamic waypoint tracking on such challenging surfaces. We leverage massively parallel simulation to train reinforcement learning agents across a vast distribution of procedurally generated environments with randomized physics. These policies are then transferred zero-shot to a physical wheeled rover operating in a lunar-analogue facility. Our experiments systematically compare multiple reinforcement learning algorithms and action smoothing filters to identify the most effective combinations for real-world deployment. Crucially, we provide strong empirical evidence that agents trained with procedural diversity achieve superior zero-shot performance compared to those trained on static scenarios. We also analyze the trade-offs of fine-tuning with high-fidelity particle physics, which offers minor gains in low-speed precision at a significant computational cost. Together, these contributions establish a validated workflow for creating reliable learning-based navigation systems, marking a substantial step towards deploying autonomous robots in the final frontier.

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