Shigeo Morishima

Improving Imitation Learning Efficiency for Manipulation through Geometric Prior Pretraining

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...