han-human-feedback-adapter-model-v1

Human Feedback Adapter Model

This model defines a lightweight behavior adapter that allows a humanoid agent to adjust its actions based on direct human feedback.

It focuses on interpretation and response logic, not large-scale model training.

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Motivation

Humanoid robots often receive short, informal corrections from humans during task execution.

This model explores how such feedback can be interpreted and used to refine behavior in real time.

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Model Scope

This is a conceptual behavior model describing:

• Feedback parsing
• Action adjustment
• Response confirmation

It does not claim to be a fully trained neural network.

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Input

• Original action
• Human feedback (text or signal)

Output

• Adjusted action plan
• Confirmation state

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Implementation Notes

The behavior rules and logic were designed manually based on realistic interactions.

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Use Cases

• Real-time correction handling
• Human-in-the-loop robotics
• Adaptive task execution

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Part of

Humanoid Network (HAN)

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License

MIT