open-rotor-copilot

Open Rotor Copilot: Neural Blackbox Analysis

Model Card for rbarac/open-rotor-copilot

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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

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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

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How to Get Started with the Model

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'])