Covariance Alignment for Robotic Trajectory Adaptation

Summary

This preprint proposes using second-order CORAL (Correlation Alignment) as an unsupervised domain adaptation step to recover trajectory classification accuracy in a parallel delta robot when payload mass and speed change. The study reports that accuracy improves from approximately 34% to over 98% after alignment, and that the Wasserstein distance drops to nearly zero.

Why it matters

Industrial robots routinely operate under varying load and speed regimes. State monitoring models trained on a single base condition can silently degrade due to covariate shift. In embedded or edge deployment scenarios where retraining or target labels are unavailable, an adaptation step that relies solely on unlabeled data and linear algebra offers an attractive option. If the results generalize beyond a single delta robot test platform, this provides a cheap and interpretable solution for a common failure mode in robotic sensing pipelines.

Highlights

• CORAL alignment boosts cross-domain trajectory classification from 34.4% to over 98% with low 10-fold cross-validation variance
• MMD decreases by 59.3% at low speed and 91.5% at high speed; the 2-Wasserstein distance is reported as completely eliminated
• The method requires no target domain labels or non-linear parameter estimation, thereby minimizing computational cost

Critical take

A '100% reduction' in 2-Wasserstein distance implies that the two distributions have become identical. This is an extraordinary claim for a simple linear covariance alignment, and it raises questions about how the metrics were calculated and whether the feature space has effectively collapsed. Validation relies on a single delta robot platform with only two load and two speed conditions. Therefore, the generalizability of these headline figures to multi-axis, multi-task industrial cells remains unproven. Furthermore, the work is an unreviewed preprint, so the statistical robustness behind the >98% accuracy figure has not yet been independently verified.