Instructions to use Spa-Bench/spa-bench-model-groot-n1-7-full-finetune-epoch-12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Spa-Bench/spa-bench-model-groot-n1-7-full-finetune-epoch-12 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
GR00T-N1.7 Full Fine-Tune — Spa-Bench Epoch 12
This checkpoint is released as an anonymous supplementary artifact for a paper under double-blind review. It was evaluated on a physical SO-101 robot in Spa-Bench.
Model details
| Field | Value |
|---|---|
| Checkpoint | End of epoch 12; step 76,596 |
| Inputs | Middle RGB, wrist RGB, six joint positions, and a text instruction |
| Outputs | Six absolute joint-position targets |
| Action horizon | 16 |
| Adaptation | Language, visual, projector, VLLN, and diffusion-action modules updated |
| Optimizer | AdamW; learning rate 1e-5; weight decay 1e-5 |
Training-data documentation: spa-bench-training-teleoperation-1200.
Physical evaluation
25/120 familiar trials succeeded. Evaluation stopped before the withheld-composition protocol, so this is a partial result. These are physical rollout results, not simulation metrics.
Rollouts: spa-bench-eval-rollouts-groot-n1-7-full-finetune-partial.
Limitations and safety
Training used a two-camera projection of the 1,200-episode release. This condition received a small upward initialization assist, limiting direct ablation claims. Robot policies can move hardware unexpectedly. Use conservative limits, an accessible emergency stop, a clear workspace, and direct supervision. Do not deploy unattended or in safety-critical settings.
Double-blind release note
Author, institution, source-repository, and archival citation details are intentionally omitted during review. They will be restored in the archival release.
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Model tree for Spa-Bench/spa-bench-model-groot-n1-7-full-finetune-epoch-12
Base model
nvidia/GR00T-N1.7-3B