Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913108747878400 |
|---|---|
| author | Isaku, Erblin Sartaj, Hassan Ali, Shaukat Sanguino, Beatriz Wang, Tongtong Li, Guoyuan Zhang, Houxiang Peyrucain, Thomas |
| author_facet | Isaku, Erblin Sartaj, Hassan Ali, Shaukat Sanguino, Beatriz Wang, Tongtong Li, Guoyuan Zhang, Houxiang Peyrucain, Thomas |
| contents | Self-adaptive robots (SARs) in complex, uncertain environments must proactively detect and address abnormal behaviors, including out-of-distribution (OOD) cases. To this end, digital twins offer a valuable solution for OOD detection. Thus, we present a digital twin-based approach for OOD detection (ODiSAR) in SARs. ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification. By combining reconstruction error with predictive variance, the digital twin effectively detects OOD behaviors, even in previously unseen conditions. The digital twin also includes an explainability layer that links potential OOD to specific SAR states, offering insights for self-adaptation. We evaluated ODiSAR by creating digital twins of two industrial robots: one navigating an office environment, and another performing maritime ship navigation. In both cases, ODiSAR forecasts SAR behaviors (i.e., robot trajectories and vessel motion) and proactively detects OOD events. Our results showed that ODiSAR achieved high detection performance -- up to 98\% AUROC, 96\% TNR@TPR95, and 95\% F1-score -- while providing interpretable insights to support self-adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins Isaku, Erblin Sartaj, Hassan Ali, Shaukat Sanguino, Beatriz Wang, Tongtong Li, Guoyuan Zhang, Houxiang Peyrucain, Thomas Robotics Artificial Intelligence Software Engineering Self-adaptive robots (SARs) in complex, uncertain environments must proactively detect and address abnormal behaviors, including out-of-distribution (OOD) cases. To this end, digital twins offer a valuable solution for OOD detection. Thus, we present a digital twin-based approach for OOD detection (ODiSAR) in SARs. ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification. By combining reconstruction error with predictive variance, the digital twin effectively detects OOD behaviors, even in previously unseen conditions. The digital twin also includes an explainability layer that links potential OOD to specific SAR states, offering insights for self-adaptation. We evaluated ODiSAR by creating digital twins of two industrial robots: one navigating an office environment, and another performing maritime ship navigation. In both cases, ODiSAR forecasts SAR behaviors (i.e., robot trajectories and vessel motion) and proactively detects OOD events. Our results showed that ODiSAR achieved high detection performance -- up to 98\% AUROC, 96\% TNR@TPR95, and 95\% F1-score -- while providing interpretable insights to support self-adaptation. |
| title | Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins |
| topic | Robotics Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2509.12982 |