| # Humanoid-Web3-IntentNet-v2 |
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| ## Overview |
| Humanoid-Web3-IntentNet-v2 is an advanced lightweight NLP model built to translate natural language commands into structured humanoid robot action intents. |
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| The model is optimized for robotics automation and Web3-based AI integration, enabling real-time action execution and blockchain logging compatibility. |
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| ## Model Architecture |
| - Base Model: DistilBERT |
| - Framework: PyTorch |
| - Task: Intent Classification |
| - Language: English |
| - Max Sequence Length: 128 |
| - Labels: 6 action classes |
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| ## Supported Actions |
| - move_object |
| - pick_and_place |
| - rotate_object |
| - scan_object |
| - start_action |
| - stop_action |
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| ## Example |
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| Input: |
| "Scan the QR code and move the device to the table." |
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| Output: |
| { |
| "action": "scan_object", |
| "object": "QR code", |
| "destination": "table" |
| } |
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| ## Use Cases |
| - Humanoid robotics control |
| - Web3 AI agents |
| - Smart factory automation |
| - Blockchain-based activity logging |
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| ## Tags |
| robotics |
| humanoid-ai |
| web3-ai |
| intent-classification |
| automation |
