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
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license: apache-2.0
library_name: lerobot
tags:
- robot
- imitation-learning
- act
- nexarm
- real-robot
---
# NexArm ACT: Pick and Place
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.
## Training Details
| Item | Value |
|------|-------|
| **Policy** | ACT (Action Chunking with Transformers) |
| **Dataset** | 31 episodes, ~27K frames |
| **Cameras** | 2× USB cameras (front + wrist), 640×480 |
| **Training Time** | ~29K steps on NVIDIA RTX 5070 Ti |
| **Final Loss** | ~0.057 |
## Usage
```python
from lerobot.policies import ACTPolicy
from lerobot.rollout import rollout
policy = ACTPolicy.from_pretrained("CypherChen/NexarmControlModel")
# Run inference on your NexArm robot
rollout(policy, robot_type="nexarm_follower")
```
## Hardware
- **Robot**: Hiwonder NexArm (ESP32 + AT32 dual-chip)
- **Servos**: HX-series serial bus servos
- **Leader arm**: Drag-to-teach teleoperation
- **Follower arm**: Executes learned policy
## License
Apache 2.0
|