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Cybernetic-Labs/openarm-packingbench-v1-groot-n17-90k-350ep
openarm-packingbench-v1-groot-n17-90k-350ep is a robotics model from Cybernetic-Labs. Use it for the robotics task on the model card, and read the license before you ship it in a product. The card lists the license as other.
Fine-tune of NVIDIA GR00T N1.7 (3B) for a bimanual OpenArm pick-and-pack task. This repo holds the inference checkpoint at 90,000 steps.
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From the Hugging Face model README
Fine-tune of NVIDIA GR00T N1.7 (3B) for a bimanual OpenArm pick-and-pack task. This repo holds the inference checkpoint at 90,000 steps.
nvidia/GR00T-N1.7-3B (vision-language backbone: nvidia/Cosmos-Reason2-2B)openarm_dataset_1_groot_lerobot_350_mixed (LeRobot v2.0)front, high_opposite, wrist_left, wrist_right| Setting | Value |
|---|---|
| Steps | 90,000 (this checkpoint) |
| Global batch size | 8 |
| Gradient accumulation | 1 |
| Save interval | every 30k (30k / 60k / 90k) |
| Embodiment tag | new_embodiment |
| Final train loss | ~0.033 |
| GPU | 1× RTX 6000 Ada (49 GB) |
Launched with gr00t/experiment/launch_finetune.py (Isaac-GR00T n1.7-release, commit 23ace64),
absolute modality config, --save-only-model.
model-0000{1,2,3}-of-00003.safetensors + model.safetensors.index.json — weightsconfig.json, experiment_cfg/, processor_config.json, embodiment_id.json — model/inference configstatistics.json — normalization statisticstrainer_state.json, training_args.bin — training metadataEvaluated in Isaac Sim via a GR00T policy server + simulated rollout
(two-object packing scene: ShippingBoxSmall + Stapler6).
Inference uses --exec-horizon 8 with the OpenArm 4-cam bimanual data config.