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--- |
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license: apache-2.0 |
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tags: |
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- robotics |
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- manipulation |
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- lerobot |
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- groot |
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- nvidia |
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- lora |
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- pick-and-place |
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base_model: nvidia/GR00T-N1.5-3B |
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datasets: |
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- so101-safe-worker |
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pipeline_tag: robotics |
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--- |
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# GR00T Pick and Place Cube v1 |
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A fine-tuned **NVIDIA GR00T N1.5** model for robotic pick-and-place manipulation tasks. |
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## Model Description |
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This model was fine-tuned using **LoRA** (Low-Rank Adaptation) on the SO-101 robot arm dataset for cube pick-and-place tasks. |
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### Training Details |
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| Parameter | Value | |
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|-----------|-------| |
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| Base Model | nvidia/GR00T-N1.5-3B | |
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| Fine-tuning Method | LoRA | |
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| LoRA Rank | 64 | |
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| LoRA Alpha | 16 | |
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| Training Steps | 50,000 | |
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| Batch Size | 8 | |
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| Dataset | 21,557 episodes / 1.9M frames | |
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| Task | Pick up cube and place in bin | |
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| Cameras | Front + Wrist (128x128) | |
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| Action Space | 4D (x, y, z, gripper) | |
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### Performance |
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- **Training Loss**: 1.17 → 0.12 (90% reduction) |
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- **Evaluation Success Rate**: ~60% (with proper action unnormalization) |
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## Usage |
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```python |
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from lerobot.policies.groot.modeling_groot import GrootPolicy |
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# Load the model |
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policy = GrootPolicy.from_pretrained("gpudad/groot-pick-place-cube-v1") |
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policy.to("cuda") |
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policy.eval() |
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# Use for inference |
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action = policy.select_action(observation_batch) |
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``` |
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### Important: Action Unnormalization |
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The model outputs actions in normalized [-1, 1] space. For the SO-101 robot: |
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- XYZ: [-1, 1] |
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- Gripper: needs mapping from [-1, 1] to [0, 2] |
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```python |
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# Unnormalize actions |
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action_min = torch.tensor([-1, -1, -1, 0]) |
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action_max = torch.tensor([1, 1, 1, 2]) |
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unnormalized = (action + 1) / 2 * (action_max - action_min) + action_min |
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``` |
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## Framework |
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Trained using [LeRobot](https://github.com/huggingface/lerobot) 🤖 |
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## License |
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Apache 2.0 (same as base GR00T model) |
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