Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media

TL;DR

This paper develops a sim-to-real framework using reinforcement learning and procedural simulation to enable autonomous wheel-based robots to navigate complex granular terrains reliably in real-world planetary exploration scenarios.

Contribution

It introduces a comprehensive workflow combining procedural simulation and reinforcement learning for robust zero-shot transfer of navigation policies to granular media.

Findings

Procedurally trained policies outperform static scenario training.

Zero-shot transfer to physical rover is successful.

Fine-tuning with high-fidelity physics yields minor improvements.

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.