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
