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.
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.
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.
Input
- Original action
- Human feedback (text or signal)
Output
- Adjusted action plan
- Confirmation state
Implementation Notes
The behavior rules and logic were
designed manually based on realistic interactions.
Use Cases
- Real-time correction handling
- Human-in-the-loop robotics
- Adaptive task execution
Part of
Humanoid Network (HAN)
License
MIT
