Instructions to use CypherChen/NexarmControlModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use CypherChen/NexarmControlModel with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Add README
Browse files
README.md
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license: apache-2.0
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---
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license: apache-2.0
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library_name: lerobot
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tags:
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- robot
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- imitation-learning
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- act
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- nexarm
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- real-robot
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---
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# NexArm ACT: Pick and Place
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This model is trained with [ACT (Action Chunking with Transformers)](https://arxiv.org/abs/2304.13705) on a **Hiwonder NexArm** 6-DOF robotic arm for a pick-and-place task.
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## Training Details
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| Item | Value |
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|------|-------|
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| **Policy** | ACT (Action Chunking with Transformers) |
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| **Dataset** | 31 episodes, ~27K frames |
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| **Cameras** | 2× USB cameras (front + wrist), 640×480 |
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| **Training Time** | ~100K steps on NVIDIA RTX 5070 Ti |
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| **Final Loss** | ~0.057 |
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## Usage
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```python
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from lerobot.policies import ACTPolicy
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from lerobot.rollout import rollout
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policy = ACTPolicy.from_pretrained("CypherChen/NexarmControlModel")
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# Run inference on your NexArm robot
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rollout(policy, robot_type="nexarm_follower")
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```
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## Hardware
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- **Robot**: Hiwonder NexArm (ESP32 + AT32 dual-chip)
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- **Servos**: HX-series serial bus servos
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- **Leader arm**: Drag-to-teach teleoperation
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- **Follower arm**: Executes learned policy
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## License
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Apache 2.0
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