—Real-time anomaly detection is pivotal to the success of smart robotics, particularly in production plants, where even minor system failures can result in significant machine downtime and costly process disruptions. To address this, a specialized Machine Learning (ML) model must be seamlessly integrated into a network of interconnected machinery, sensors, and actuators, all processing vast streams of multidimensional sensor data with minimal latency that cloud-based solutions often struggle to achieve. In this work, we introduce VARADE++, an edge-optimized anomaly detection framework designed to navigate the complex trade-offs between anomaly detection accuracy, inference speed, and computational effi-ciency. By leveraging a lightweight auto-regressive architecture rooted in attentionless transformers, paired with a variational training paradigm, we achieve real-time processing capabilities. Our model is integrated into an advanced IoT infrastructure, enabling low-latency handling of intricate data streams. The effectiveness of VARADE++ is demonstrated across two public benchmarks and validated through a real-world case study within a sensorized industrial pilot production line, with an industrial robot as the primary focus. Our results not only highlight the superior anomaly detection capabilities of VARADE++ but also showcase its operational efficiency in a real-time edge
Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio et al.· IEEE Transactions on Emergin...· 0 citations
: This paper introduces a cognitively inspired neuro-symbolic framework for extracting interpretable world models from raw video streams in dynamic 2D environments. While traditional end-to-end deep reinforcement learning systems often function as opaque “black boxes,” our approach decouples visual perception from policy learning to enhance transparency. By leveraging Core Knowledge theory (specifically object persistence, physical causality, and agent representation), the system transforms visual patches into structured symbolic entities and governing interaction rules. The core of this architecture is a symbolic module that reconstructs persistent objects, infers parametric motion laws (such as velocity inversion upon contact), and incrementally consolidates class-specific behaviors into a compact knowledge base. This world model instantiates a domain-agnostic environment wrapper, enabling a Deep Q-Network to operate on symbolic state vectors rather than pixels. We further propose a domain-agnostic agent that develops complex behaviors driven by a composite intrinsic reward based on causal impact and event-driven curiosity. Experimental evaluations on Arkanoid and Pong demonstrate that this framework achieves near-optimal performance without task-specific external rewards. On Arkanoid, the agent matches the win rate of fully supervised models while remaining robust to structural modifications, such as changes in ball size or brick configuration. In Pong, the same mechanism transfers without architectural adjustments, consistently improving survival times. These results provide a transparent, generalizable alternative to dominant AI paradigms with good performance across varied environmental conditions.
Lorenzo Cardone, Giorgia Ghisolfo, G. Mongardi et al.· International Conference on...· 0 citations
CP-McSplitDAL is introduced, a cooperative parallel framework that extends McSplit-DAL with portfolio-style multi-heuristic search on shared-memory machines and achieves lower regret in time to optimality, improves solution quality under time limits, and better exploits multi-core hardware than non-cooperative or purely sequential variants.
Lorenzo Cardone, Stefano Quer· International Conference on...· 0 citations
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