---
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.