Research
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.
Mobile ManipulationImitation LearningNovel-View Synthesis