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README.md

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