FATHOM: World-Model-Guided Diffusion Action-Trajectory Selection for Sparse-Marker AUV Tracking
Overview
This research introduces FATHOM, a framework designed for reliable relative tracking of autonomous underwater vehicles (AUVs) using sparse active light markers. The system combines structured marker observations, inertial data, and action history within a multimodal recurrent state-space model to form a predictive tracking belief. A diffusion action prior generates multiple candidate trajectories, which are then evaluated against predicted future consequences via learned latent dynamics. FATHOM selects the optimal trajectory using a receding-horizon objective and updates decisions based on onboard observations. Extensive offline analyses and real-AUV experiments demonstrate robust autonomous tracking under nominal and challenging conditions, including partial marker visibility and dynamic motion, all while maintaining real-time operation without external motion-capture feedback.
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“ive offline analyses verify the learned predictive belief, multi-step world-model prediction, diffusion trajectory generation, and predictive selection. More importantly, comprehensive real-AUV experiments, including strong pose-based comparisons and component ablations, demonstrate robust autonomous tracking across nominal and challenging motions with naturally occurring partial marker visibility, while maintaining”
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- arXiv Robotics — research abstractsSignalFATHOM: World-Model-Guided Diffusion Action-Trajectory Selection for Sparse-Marker AUV Tracking
This research introduces FATHOM, a framework designed for reliable relative tracking of autonomous underwater vehicles (AUVs) using sparse active light markers. The system combines structured marker observations, inertial data, and action history within a multimodal recurrent state-space model to form a predictive tracking belief. A diffusion action prior generates multiple candidate trajectories, which are then evaluated against predicted future consequences via learned latent dynamics. FATHOM selects the optimal trajectory using a receding-horizon objective and updates decisions based on onboard observations. Extensive offline analyses and real-AUV experiments demonstrate robust autonomous tracking under nominal and challenging conditions, including partial marker visibility and dynamic motion, all while maintaining real-time operation without external motion-capture feedback.
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