S-AI-ROBOTICS: A SPARSE ARTIFICIAL INTELLIGENCE ARCHITECTURE WITH HORMONAL ORCHESTRATION, PARSIMONIOUS CONTROL, AND SYMBOLIC MEMORY FOR ADAPTIVE, SAFE, AND EXPLAINABLE EMBODIED ROBOTICS

Traditional monolithic and learning-based robotic control architectures often demand continuous computational activation, high energy consumption, and opaque decision-making processes, which limits their deployment in safety-critical, resource-constrained environments. To address these challenges, the source paper introduces S-AI-ROBOTICS, a modular, bio-inspired robotic intelligence framework based on Sparse Artificial Intelligence (S-AI). Instead of always-on execution, the architecture implements context-aware parsimony. Specialized robotic agents are activated dynamically only when triggered by a symbolic hormonal state representing urgency, stability, energy, and confidence. A Robo-MetaAgent manages this activation using constrained optimization and hysteresis-based dynamics to prevent rapid, unstable switching. The framework integrates an artificial hormonal signaling layer modeled after neuroendocrine systems, utilizing bounded emission, inhibition, diffusion,

and decay to regulate behavioral priorities. Concurrently, a symbolic and contextual memory subsystem records behavioral engrams linking hormonal contexts, active agents, actions, and outcomes to provide native explainability. While the paper outlines this robust theoretical and structural model, practical implementation details, specific hardware-in-the-loop validation, and physical energy-saving benchmarks remain areas requiring further empirical evaluation and structuring.

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