Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins

• URL: http://arxiv.org/abs/2509.12982v1
• Date: Tue, 16 Sep 2025 11:43:47 GMT
• Title: Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins
• Authors: Erblin Isaku, Hassan Sartaj, Shaukat Ali, Beatriz Sanguino, Tongtong Wang, Guoyuan Li, Houxiang Zhang, Thomas Peyrucain,
• Abstract summary: We present a digital twin-based approach for OOD detection in self-adaptive robots (SARs)<n>ODiSAR uses a Transformer-based digital twin to forecast SAR states and employs reconstruction error and Monte Carlo dropout for uncertainty quantification.<n>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.
• Score: 9.669021722603958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Dynamic Aware: Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles [8.920589816043298]
  Trajectory prediction is central to the safe and seamless operation of autonomous vehicles.<n>In deployment, prediction models inevitably face distribution shifts between training data and real-world conditions.<n>We propose a new framework that introduces adaptive mechanisms to achieve robust detection in complex driving environments.
  arXiv  Detail & Related papers  (2025-09-16T22:37:21Z)
• Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels [3.229371159969159]
  An autonomous vessel (AV) is a complex cyber-physical system (CPS) with software enabling many key functionalities.<n>Digital twins of such AVs enable advanced functionalities such as running what-if scenarios, performing predictive maintenance, and enabling fault diagnosis.<n>We present a novel digital twin-based approach (ODDIT) to detect future out-of-distribution (OOD) states of an AV before reaching them.
  arXiv  Detail & Related papers  (2025-04-28T14:12:46Z)
• STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability [9.658282892513386]
  Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences.<n>We propose STOOD-X, a two-stage methodology that combines a Statistical nonparametric Test for OOD Detection with eXplainability enhancements.<n>In the first stage, STOOD-X uses feature-space distances and a Wilcoxon-Mann-Whitney test to identify OOD samples without assuming a specific feature distribution.<n>In the second stage, it generates user-friendly, concept-based visual explanations that reveal the features driving each decision, aligning with the BLUE
  arXiv  Detail & Related papers  (2025-04-03T15:26:03Z)
• Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection [63.93728560200819]
  Unsupervised out-of-distribution (U-OOD) detection is to identify data samples with a detector trained solely on unlabeled in-distribution (ID) data. Recent studies have developed various detectors based on DGMs to move beyond likelihood. We apply two techniques for each direction, specifically post-hoc prior and dataset entropy-mutual calibration. Experimental results demonstrate that the Resultant could be a new state-of-the-art U-OOD detector.
  arXiv  Detail & Related papers  (2024-09-05T02:58:13Z)
• Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill [0.32985979395737786]
  The digital twin consists of three modules emulating a closed-loop system. The model was trained with 68 hours of data and validated with 8 hours of test data. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system.
  arXiv  Detail & Related papers  (2024-07-04T17:20:36Z)
• Distilling the Unknown to Unveil Certainty [66.29929319664167]
  Out-of-distribution (OOD) detection is critical for identifying test samples that deviate from in-distribution (ID) data, ensuring network robustness and reliability.<n>This paper presents a flexible framework for OOD knowledge distillation that extracts OOD-sensitive information from a network to develop a binary classifier capable of distinguishing between ID and OOD samples.
  arXiv  Detail & Related papers  (2023-11-14T08:05:02Z)
• DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data [7.712429926730386]
  Causal deep learning has improved over traditional techniques for estimating individualized treatment effects. We present DR-VIDAL, a novel generative framework that combines two joint models of treatment and outcome. DR-VIDAL achieves better performance than other non-generative and generative methods on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-07T19:44:58Z)
• Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift [108.30303219703845]
  We find that ID-calibrated ensembles outperforms prior state-of-the-art (based on self-training) on both ID and OOD accuracy. We analyze this method in stylized settings, and identify two important conditions for ensembles to perform well both ID and OOD.
  arXiv  Detail & Related papers  (2022-07-18T23:14:44Z)
• Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data [77.27338842609153]
  characterization reveals that sensitivity to each type of shift is important to the detection and confidence calibration of OOD data. We propose a geometrically-inspired method to improve OOD detection under both shifts with only in-distribution data. We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts.
  arXiv  Detail & Related papers  (2021-10-28T15:42:55Z)
• Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications [2.9327503320877457]
  Out-of-distribution (OD) detection is an emerging approach to address the challenge of detecting out-of-distribution in real-time. In this paper, we show how we can robustly detect hazardous motion around autonomous driving agents. Our methods significantly improve detection capabilities of OoD factors to unique driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented.
  arXiv  Detail & Related papers  (2021-07-25T07:52:53Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.