MOMASPLAT: Dynamic Novel-View Demonstration Augmentation for Mobile Manipulation
Overview
This research presents MOMASPLAT, a framework designed to augment demonstration data for mobile manipulation by synthesizing trajectories from unseen robot base poses. The method combines 3D Gaussian Splatting and feed-forward 3D foundation models to reconstruct static scenes and manipulated objects in 3D, enabling the generation of geometrically consistent synthetic demonstrations. Evaluated on five real-world tasks involving rigid and articulated objects across three scenes, MOMASPLAT demonstrates improved robustness to navigation uncertainty and localization drift, more than doubling the average success rate of baseline methods while reducing the need for large-scale data collection.
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“ted object, render them from novel viewpoints, and generate geometrically consistent synthetic demonstrations paired with transformed actions. We evaluate MOMASPLAT on five real-world mobile manipulation tasks involving both rigid and articulated objects across three scenes, and validate the fidelity of its synthesized demonstrations against ground truth in simulation. MOMASPLAT substantially improves robustness to u”
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- arXiv Robotics — research abstractsSignalMOMASPLAT: Dynamic Novel-View Demonstration Augmentation for Mobile Manipulation
This research presents MOMASPLAT, a framework designed to augment demonstration data for mobile manipulation by synthesizing trajectories from unseen robot base poses. The method combines 3D Gaussian Splatting and feed-forward 3D foundation models to reconstruct static scenes and manipulated objects in 3D, enabling the generation of geometrically consistent synthetic demonstrations. Evaluated on five real-world tasks involving rigid and articulated objects across three scenes, MOMASPLAT demonstrates improved robustness to navigation uncertainty and localization drift, more than doubling the average success rate of baseline methods while reducing the need for large-scale data collection.
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