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

• URL: http://arxiv.org/abs/2504.11170v2
• Date: Wed, 16 Apr 2025 08:50:55 GMT
• Title: A Real-time Anomaly Detection Method for Robots based on a Flexible and Sparse Latent Space
• Authors: Taewook Kang, Bum-Jae You, Juyoun Park, Yisoo Lee,
• Abstract summary: Deep learning-based models in robotics face challenges due to limited training data and highly noisy signal features.<n>We present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoders model to address these problems.<n>Our model performs inferences within 1 millisecond, ensuring real-time anomaly detection.
• Score: 2.0186752447895993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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