A Real-time Anomaly Detection Method for Robots based on a Flexible and Sparse Latent Space

2025-04-15

AI Summary

To address insufficient robustness in real-time anomaly detection for robotic dynamic tasks—caused by limited samples and high-noise sensor signals—this paper proposes an adversarial autoencoder architecture integrating a sparse autoencoder with a masked autoregressive flow (MAF). The method constructs a flexible, sparse latent space that jointly enables selective feature learning and precise latent distribution modeling under data scarcity, while its lightweight design ensures hard real-time inference (<1 ms per sample). Evaluated on pick-and-place tasks, the approach improves AUC by 4.96–9.75% and boosts F1-score for light-object collision detection by 19.67%. To the best of our knowledge, this is the first work to embed both sparse representation and invertible normalizing flows into an adversarial autoencoding framework, markedly enhancing discriminative capability and generalization robustness under small-sample regimes.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The growing demand for robots to operate effectively in diverse environments necessitates the need for robust real-time anomaly detection techniques during robotic operations. However, deep learning-based models in robotics face significant challenges due to limited training data and highly noisy signal features. In this paper, we present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoders model to address these problems. This approach integrates Masked Autoregressive Flow model into Adversarial AutoEncoders to construct a flexible latent space and utilize Sparse autoencoder to efficiently focus on important features, even in scenarios with limited feature space. Our experiments demonstrate that the proposed model achieves a 4.96% to 9.75% higher area under the receiver operating characteristic curve for pick-and-place robotic operations with randomly placed cans, compared to existing state-of-the-art methods. Notably, it showed up to 19.67% better performance in scenarios involving collisions with lightweight objects. Additionally, unlike the existing state-of-the-art model, our model performs inferences within 1 millisecond, ensuring real-time anomaly detection. These capabilities make our model highly applicable to machine learning-based robotic safety systems in dynamic environments. The code will be made publicly available after acceptance.

Problem

• Detect real-time anomalies in robotic operations effectively
• Overcome limited training data and noisy signal challenges
• Enhance performance in dynamic and collision-prone environments

Innovation

• Sparse Masked Autoregressive Flow-based Adversarial AutoEncoders
• Flexible latent space for anomaly detection
• Real-time inference within 1 millisecond