| --- |
| license: other |
| license_name: nvidia-open-model-license |
| license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ |
| base_model: nvidia/GR00T-N1.7-3B |
| tags: [robotics, gr00t, sh5, tactile] |
| --- |
| # GR00T N1.7 SH5 Task000905 — 100,000 steps |
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|
| Complete resumable checkpoint at repository root. |
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| Task: Use the right hand to pick up a white sanitary pad from the box and hold it. |
| 51 recorded episodes, 46 train / 5 held out, 30 Hz. Head camera only |
| (`cam_left_head`), 54 measured joint positions and 90 raw tactile values. |
| Outputs: 40 x 54 absolute joint positions in radians, ordered left arm7, |
| right arm7, left hand20, right hand20. Head/lift/base are excluded from outputs. |
|
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| Batch64, accumulation1, LR2e-05, warmup1000, BF16/TF32. Frozen VLM backbone; |
| action/projectors and finger_mlp tactile encoder are trained. See |
| `artifact_manifest.json` for the source commit, normalization and contract. |
| This requires the compatible custom SH5 tactile GR00T implementation, not |
| vanilla GR00T without the tactile adapter. No robot success/safety claim is made. |
|
|
| When ready, model/processor/statistics/embodiment mapping and optimizer, |
| scheduler, RNG, trainer step are included. Raw videos, demonstrations, |
| credentials, server paths, private logs and TrainingArguments pickle are excluded. |
| Verify `SHA256SUMS` after downloading every file at the recorded commit. |
|
|
| Resume: download this repository completely to `checkpoint-100000` inside the |
| original run, retain the original local dataset and launcher, and use |
| `train_r2.sh --resume`. Restore the saved optimizer/scheduler/RNG and |
| continue the original 200000-update cosine schedule. Offline inference |
| uses Gr00tPolicy with embodiment `new_embodiment` and `use_tactile=True`. |
|
|
