SOURCE-LINKED INTELLIGENCE

Improving Imitation Learning Efficiency for Manipulation through Geometric Prior Pretraining

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Applying an imitation learning policy to a new manipulation task usually requires collecting new demonstrations and retraining the model, which makes sample efficiency a practical concern. Pretraining on large-scale robot datasets is effective in this respect, but such datasets are costly to collect and train on, while data augmentation techniques typically require a new round of data generation and retraining for each task. A complementary question is what useful prior can be provided to a policy at negligible cost before any task-specific data are collected. In this study, we construct a geo

Read original source ↗ Open in workspace

recordType

paper

region

Global