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move-soft-toy-right-groot-sharednorm
omkarpatil/move-soft-toy-right-groot-sharednorm
move-soft-toy-right-groot-sharednorm is a robotics model from omkarpatil. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for lerobot. The card lists the license as apache-2.0.
Fine-tune of nvidia/GR00T-N1.7-3B on move-soft-toy-right for the ROBOTIS FFW SG2 Rev1, trained with the shared-norm recipe.
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At a glance
- Task
- Robotics
- Library
- lerobot
- License
- apache-2.0
- Model type
- Gr00tN1d7
- Access
- Public
- Created
- Sep 3, 2026
- Updated
- Sep 3, 2026
- SHA
- 02853f13
Base models
Model card
From the Hugging Face model README
GR00T N1.7 — move-soft-toy-right (shared-norm)
Fine-tune of nvidia/GR00T-N1.7-3B on
move-soft-toy-right for the ROBOTIS FFW SG2 Rev1, trained with the shared-norm recipe.
Composition group C
Members: move-soft-toy-left, move-soft-toy-right. Normalization statistics were pooled over 5,249 frames of all
group members and written identically into each member's dataset, so every policy in the
group applies the same invertible transform and their scores can be composed.
sha256(new_embodiment stats)[:16] = a9a2b7939222c30e
Only compose models reporting this same hash. Other groups (push / pick-handover / soft-toy) each have their own transform and are not mutually composable.
Training — fully stock
No code patches. Only the dataset statistics differ from a default fine-tune.
| Entrypoint | gr00t/experiment/launch_finetune.py (unmodified) |
| Normalization | q01/q99 min-max → [-1, 1], use_percentiles=True, clip_outliers=True |
| Precision | fp32 (load_bf16=False, stock) |
| Embodiment | NEW_EMBODIMENT — FFW SG2 Rev1, state 22 / action 16 |
| Cameras | cam_left_head, cam_left_wrist, cam_right_wrist |
| Data rate | 15 fps, 16-step action chunk (≈1.07 s) |
| Episodes / frames | 21 / 2674 |
| Steps | 20 000, lr 1e-4, warmup 0.05, weight decay 1e-5, batch 32 |
| Final train loss | 0.042048356426134706 |
Attention uses PyTorch sdpa, not flash-attention-2 (host glibc predates the wheel's requirement). Both are exact attention; results are not bit-reproducible against a flash-attn build.
Inference-ready only — optimizer state and intermediate checkpoints are excluded, so this cannot resume training.
- Task
- Robotics
- Library
- lerobot
- Type
- Gr00tN1d7
- License
- apache-2.0
- Created
- Sep 3, 2026
- Updated
- Sep 3, 2026