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license: apache-2.0
pipeline_tag: robotics
tags:
- robotics
- gr00t
- vla
- bimanual
- deformable-object-manipulation
- cloth
---
# GR00T N1.7 — flatten_tshirt (bimanual cloth flattening)
An [Isaac GR00T](https://github.com/NVIDIA/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`](https://huggingface.co/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.
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