han-humanoid-temporal-stability-forecaster-v1

Humanoid Temporal Stability Forecaster (HTSF)

Problem Statement

During continuous human-robot interaction, stability does not degrade instantly — it drifts over time due to accumulated micro-errors and environmental fluctuations.

Most existing models treat stability as a static prediction task. HTSF models stability as a temporal forecasting problem.

Objective

Forecast short-horizon stability index for the next interaction step.

Input

Time-windowed tabular sequence:

• environmental_shift_index (t-n → t)
• command_ambiguity_score (t-n → t)
• actuator_micro_error (t-n → t)
• stability_index (t-n → t)

Output

• predicted_stability_index_t+1
• forecast_confidence_interval

Model Architecture

• Temporal feature encoder
• Gated recurrent layer (lightweight GRU)
• Stability drift regularizer
• Forecast regression head

Training Configuration

• Loss: MSE + Temporal Smoothness Penalty
• Optimizer: Adam
• Sliding window training
• Early stopping enabled

Evaluation Metrics

• MAE
• RMSE
• Forecast Drift Error
• Stability Trend Correlation

Intended Use

• Proactive stability correction
• Early intervention trigger
• Adaptive control adjustment

Limitations

• Requires sequential interaction logs
• Not designed for single-step prediction

License

MIT