Geometric Action Model for Robot Policy Learning
Paper • 2606.17046 • Published • 117
This repository contains released GAM (Geometric Action Model) checkpoints for LIBERO and LIBERO-Plus evaluation.
| Suite key | LIBERO suite | Checkpoint | Config |
|---|---|---|---|
spatial |
libero_spatial |
spatial/gam.pt |
spatial/config.yaml |
object |
libero_object |
object/gam.pt |
object/config.yaml |
goal |
libero_goal |
goal/gam.pt |
goal/config.yaml |
long |
libero_10 |
long/gam.pt |
long/config.yaml |
| Name | Checkpoint | Source |
|---|---|---|
pretrained-gam |
pretrained/pretrained-gam.pt |
SeonghuJeon/da3-shallow12-robocasa25-h8-step235000 (0235000.pt, step 235k) |
This checkpoint is provided under the simple pretrained-gam name for convenience.
It corresponds to the DA3 shallow12 RoboCasa25 H8 checkpoint at step 235000.
The configs are portable: stage_1.ckpt_path, dataset.hdf5_root, and
dataset.stats_dir resolve under ${DA3_ROOT} by default. Download
SeonghuJeon/3da-libero-training-assets into the repository root to provide
the DA3-Giant base checkpoint and LIBERO training assets.
Project page: https://cvlab-kaist.github.io/Geometric-Action-Model/ Paper: https://arxiv.org/abs/2606.17046 Code: https://github.com/JeonSeongHu/gam_test/tree/public/libero-da3giant