| --- |
| language: |
| - en |
| library_name: lerobot |
| base_model: nvidia/GR00T-N1.7-3B |
| datasets: |
| - justintiensmith/VLA_Reasoning_Training_Dataset_1200_2cam |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - vision-language-action |
| - imitation-learning |
| - so-101 |
| - spa-bench |
| - groot-n1.7 |
| license: apache-2.0 |
| --- |
| |
| # GR00T-N1.7 Full Fine-Tune — Spa-Bench Epoch 12 |
|
|
| This is the GR00T-N1.7 checkpoint evaluated as the **Full Fine-Tune** condition |
| in Spa-Bench, a real-robot benchmark of spatially grounded reasoning. |
|
|
| ## Model details |
|
|
| | Field | Value | |
| | --- | --- | |
| | Model repository | [`justintiensmith/groot_multi_gpu_v2`](https://huggingface.co/justintiensmith/groot_multi_gpu_v2) | |
| | Base model | [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B) | |
| | Checkpoint | End of epoch 12; step 76,596 | |
| | Robot | SO-101 single-arm manipulator | |
| | Inputs | Fixed middle RGB, wrist RGB, six absolute joint positions, text instruction | |
| | Outputs | Six absolute joint-position targets | |
| | Action horizon | 16 | |
| | Adaptation | Language, visual, multimodal/projector, VLLN, and diffusion-action modules updated | |
| | Optimizer | AdamW, learning rate `1e-5`, weight decay `1e-5` | |
| | Schedule | 5% warm-up, then cosine decay | |
| | Hardware and batch | Four NVIDIA GH200 GPUs; 24 samples per device, global batch 96 | |
|
|
| The embedded `train_config.json` pins the two-camera full-length training data |
| to [`justintiensmith/VLA_Reasoning_Training_Dataset_1200_2cam@b82cdc8`](https://huggingface.co/datasets/justintiensmith/VLA_Reasoning_Training_Dataset_1200_2cam/tree/b82cdc8f131f33320f38d7a990337c0fd98f1353). |
| It contains 1,200 episodes, 612,733 frames, 321 instruction strings, and the |
| middle and wrist views used by this policy. |
|
|
| The run records the base-model identifier but not its immutable source |
| revision. The current public GR00T base revision at archival review is |
| `2fc962b973bccdd5d8ce4f67cc63b264d6886495`; it must not be assumed to be the |
| unrecorded training revision. |
|
|
| The author-supplied original |
| [`train_groot_n17_full_ft.sh`](https://github.com/justintiensmith/Imperial-Thesis/blob/main/training/groot/train_groot_n17_full_ft.sh) |
| launcher is archived with the thesis artifact. It records seed 42, four-GPU |
| training, the immutable dataset revision, component-tuning flags, checkpoint |
| cadence, and the historical environment paths used for this run. |
|
|
| ## Deployment processing and intervention |
|
|
| Non-gripper action dimensions used the same causal filter as the Frozen LLM |
| variant: `filtered = 0.25 × current + 0.75 × previous_filtered`. Filter state was |
| initialized from the measured robot state and reset for every rollout. |
|
|
| This condition also received a small upward initialization assist before the |
| scored timer. A retrospective estimate found 10.09 mm mean end-effector |
| separation from the nominal start, including 7.63 mm mean upward displacement; |
| the maximum paired arm-joint difference was 6.40°. Nominal start deviations |
| were 9.27 mm for this condition and 3.09 mm for Frozen LLM. These values include |
| ordinary reset/calibration variation and the intervention is a limitation when |
| interpreting results. |
|
|
| ## Physical evaluation |
|
|
| The checkpoint completed **25/120 familiar/in-distribution trials (20.8%)**, |
| with 20 trials from each Spa-Bench task family. Evaluation was stopped before |
| the OOD and diagnostic protocol, so this checkpoint must not be compared with |
| the fully evaluated policies on the headline OOD benchmark. |
|
|
| - Rollouts: [`justintiensmith/Spa_Bench_Partial_GR00T-N1.7_Full_Fine-Tune`](https://huggingface.co/datasets/justintiensmith/Spa_Bench_Partial_GR00T-N1.7_Full_Fine-Tune) |
| - Thesis artifact: [`justintiensmith/Imperial-Thesis`](https://github.com/justintiensmith/Imperial-Thesis) |
|
|
| ## Intended use and limitations |
|
|
| This release supports reproduction and analysis of the Spa-Bench experiment. |
| Its evaluation is partial and contains no OOD trials. The reported outcome |
| applies only to this checkpoint and protocol; it is not a general assessment of |
| GR00T-N1.7. The initialization assist is unique to this condition and prevents a |
| clean parameter-freezing ablation. |
|
|
| Robot policies can move hardware unexpectedly. Use conservative motion limits, |
| an accessible emergency stop, a clear workspace, and direct supervision. Do |
| not deploy this checkpoint for unattended or safety-critical operation. |
|
|
| ## Citation |
|
|
| Please cite the [completed Spa-Bench MSc report](https://github.com/justintiensmith/Imperial-Thesis/blob/main/thesis/report/spa-bench-msc-thesis.pdf), |
| the [thesis artifact](https://github.com/justintiensmith/Imperial-Thesis), and |
| the GR00T-N1.7 work referenced in the report. |
|
|
