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MOMASPLAT: Novel-View Augmentation for Mobile Manipulation Policies

MOMASPLAT is a framework that augments imitation learning demonstrations for mobile manipulation by synthesizing full trajectories from unseen robot base poses, including dynamic object interactions. It combines 3D Gaussian Splatting, feed-forward 3D foundation models, and kinematics-based action retargeting to reconstruct static scenes and manipulated objects separately. Evaluated on five real-world tasks, it more than doubles the average success rate of the strongest baseline and maintains non-trivial success at large base displacements.

arxiv18h agoScore 78
Mobile ManipulationImitation LearningNovel-View Synthesis
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RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On

RoboTryOn converts egocentric human videos into robot demonstration videos by swapping the human for a target robot while preserving the task, objects, and background. It uses autoregressive keyframe translation and motion conditioning on human hand skeletons, plus an inverse dynamics model with depth features to infer actions. Policies trained on this synthetic data improved success rates on novel bimanual tasks from 2% to 60% without real robot demos, and matched teleoperation-trained performance at about a third of the collection cost.

Why it matters: This paper presents a novel, cost-effective method for generating robot training data from human videos, with strong experimental results, making it valuable for robotics and AI research.

arxiv2d agoScore 88
Robot foundation modelsImitation learningData augmentation