GR00T N1.7 — flatten_tshirt (bimanual cloth flattening)
An Isaac GR00T N1.7-3B vision-language-action policy
fine-tuned on the flatten_tshirt task of a bimanual deformable-object (cloth / bag)
manipulation benchmark. The robot is a dual-arm Piper; the task is to flatten a crumpled
t-shirt on a table.
Simulation uses a GPU cloth solver co-simulated with the robot in a single model, and observations are rendered with a photorealistic renderer.
⚠️ Requires access to a gated backbone
This checkpoint holds the fine-tuned GR00T weights only. Its vision-language backbone is
nvidia/Cosmos-Reason2-2B, a gated
repo — config.json references it by repo id. You must request access to that repo and be
authenticated before this model will load. Those weights are NVIDIA's and are not redistributed
here.
Model
| Architecture | Gr00tN1d7 (GR00T N1.7-3B) |
| Backbone | nvidia/Cosmos-Reason2-2B (gated, see above) |
| Precision | bfloat16 |
| Observation | 3 × RGB (static_cam, left_hand_cam, right_hand_cam) + 14-D joint state |
| Action | 14-D (left 6 joints + gripper, right 6 joints + gripper) |
| Action horizon | 40 |
| Inference timesteps | 4 |
| Image target size | 256 × 256 (crop fraction 0.95) |
| Embodiment tag | new_embodiment |
Training
| Dataset | flatten_tshirt_200 — 200 episodes / 41,464 frames, LeRobot v3.0, 25 fps |
| Steps | 20,000 |
| Global batch size | 32 |
| Image augmentation | GR00T defaults (brightness 0.3, contrast 0.4, saturation 0.5, hue 0.08, + crop) |
Unlike the other policies in this benchmark, GR00T is trained with its author-default augmentation recipe rather than a tuned one: each policy is trained with its own best-known recipe, while the data and the evaluation protocol are held identical across policies.
20,000 steps at batch 32 is the same sample budget (~640k) as the official 10,000 × 64 configuration.
What is in this repo
Inference weights only. The optimizer state, HF Trainer arguments and W&B run config from the
original checkpoint have been removed — they carry no inference value and embedded internal
infrastructure paths. trainer_state.json is kept for its loss history.
Status
⚠️ This checkpoint has not been evaluated. It has not been through the benchmark's closed-loop protocol at all. Success-rate numbers are deliberately not published here; they will be added once evaluation has run. Treat this as a training artifact, not a reported result.
License
Apache-2.0 for these fine-tuned weights. The Cosmos backbone is covered by NVIDIA's own license terms.