---
license: mit
language:
- en
base_model:
- google/flan-t5-small
---
# Open Rotor Copilot: Neural Blackbox Analysis

## Model Card for `rbarac/open-rotor-copilot`

---

## Model Details

**Model Description**

Open Rotor Copilot is an open-source neural model for automated blackbox analysis and issue detection in FPV drone flight logs. Given a windowed feature string (representing a segment of Blackbox telemetry), it predicts likely flight anomalies or faults and provides concise human-readable explanations. This model enables pilots, engineers, and researchers to automate log review and root cause analysis for multirotor platforms.

- **Developed by:** Bahadir Arac
- **Model type:** Transformer (sequence classification, text-to-label)
- **Language(s) (NLP):** English

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## Model Sources

- **Repository:** [https://huggingface.co/rbarac/open-rotor-copilot](https://huggingface.co/rbarac/open-rotor-copilot)
---

## Uses

### Direct Use

- Automated analysis of Betaflight/INAV/ArduPilot blackbox logs
- FPV drone diagnostics for issues such as vibration, motor problems, signal loss, or sensor faults
- Assisting pilots in understanding potential causes for in-flight anomalies
- Integrating into post-flight dashboards or tools (e.g., Gradio demo, local scripts)

### Downstream Use

- Research into drone reliability, flight risk, and root cause modeling
- Educational purposes (for teaching blackbox log interpretation)
- Automated labeling for LLM training

### Out-of-Scope Use

- Direct control of flight hardware (this model is for analysis only)
- Use in domains outside of drone telemetry without further evaluation

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## Bias, Risks, and Limitations

- Model accuracy depends on the representativeness of training data
- Unusual or novel failure modes not present in data may not be detected
- Not a replacement for domain expertise—should be used as an assistant, not sole authority

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## Recommendations

- Always review results, especially for safety-critical applications
- Contribute new log data or open issues to help improve the model

---

## How to Get Started with the Model

```python
from transformers import pipeline

pipe = pipeline("text-classification", model="rbarac/open-rotor-copilot")

# Example input (windowed feature string, hybrid format)
input_str = "gyro_std=123.56, rcCommand3_mean=1150.2, vbat_drop=0.91, rssi_min=17.2, ..."

result = pipe(input_str)
print("Predicted Issue(s):", result[0]['label'])