---
language:
- en
library_name: lerobot
base_model: nvidia/GR00T-N1.7-3B
datasets:
- justintiensmith/VLA_Reasoning_Training_Dataset_1200_Trimmed_Start_5_Frame
pipeline_tag: robotics
tags:
- robotics
- vision-language-action
- imitation-learning
- so-101
- spa-bench
- groot-n1.7
license: apache-2.0
---

# GR00T-N1.7 Frozen LLM — Spa-Bench Epoch 12

This is the GR00T-N1.7 checkpoint evaluated as the **Frozen LLM** condition in
Spa-Bench. “Frozen LLM” means that the language-model parameters were held
fixed; the policy continued to receive language instructions.

## Model details

| Field | Value |
| --- | --- |
| Model repository | [`justintiensmith/groot_multi_gpu_v4`](https://huggingface.co/justintiensmith/groot_multi_gpu_v4) |
| Base model | [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B) |
| Checkpoint | End of epoch 12; step 71,304 |
| 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 | LLM frozen; visual, multimodal/projector, VLLN, and diffusion-action modules updated |
| Optimizer | AdamW, learning rate `1e-4`, 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 motion-trimmed training data to
[`justintiensmith/VLA_Reasoning_Training_Dataset_1200_Trimmed_Start_5_Frame@c1231a8`](https://huggingface.co/datasets/justintiensmith/VLA_Reasoning_Training_Dataset_1200_Trimmed_Start_5_Frame/tree/c1231a8f2b282b3f875fc898dced9ebf5574b903).
That exact snapshot is a two-camera projection of the repository's five-camera
trimmed release and is preserved by the `groot-frozen-training-snapshot` tag.
The tabular trajectory file and middle/wrist videos are byte-identical between
the two snapshots; episode metadata differs because the unused camera keys were
removed. Both contain 1,200 episodes, 570,386 frames, and 321 instruction
strings.

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_frozen_llm.sh`](https://github.com/justintiensmith/Imperial-Thesis/blob/main/training/groot/train_groot_n17_frozen_llm.sh)
launcher is archived with the thesis artifact. It records seed 42, four-GPU
training, the immutable dataset revision, component-freezing flags, checkpoint
cadence, and the historical environment paths used for this run.

## Deployment processing

For both GR00T variants, non-gripper action dimensions used the causal filter
`filtered = 0.25 × current + 0.75 × previous_filtered`. Filter state was
initialized from the measured robot state and reset for every rollout. The
gripper command was excluded.

## Physical evaluation

| Condition | Successes | Rate |
| --- | ---: | ---: |
| Familiar/in-distribution spatial instructions | 81/120 | 67.5% |
| All withheld/OOD spatial configurations | 133/300 | 44.3% |
| Matched OOD subset | 50/120 | 41.7% |
| Matched direct-manipulation controls | 98/120 | 81.7% |

The matched-control gap was 40.0 percentage points. These are physical rollout
results, not simulation metrics.

- Rollouts: [`justintiensmith/Spa_Bench_Full_GR00T-N1.7_Frozen_LLM`](https://huggingface.co/datasets/justintiensmith/Spa_Bench_Full_GR00T-N1.7_Frozen_LLM)
- 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.
Older project files may call it “vision-only”; that term is inaccurate because
several non-language modules were updated. The motion-trimmed data and
deployment smoothing are policy-specific choices, so this comparison is not a
controlled architecture 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.