Preenchendo a Lacuna da Supervisão Humana na Robótica por Meio da Linguagem: Uma IA Agêntica Multimodal para Escalar a Supervisão Humana em Sistemas Autônomos

Abstract

While many autonomous systems can navigate and avoid obstacles under predictable conditions, they often rely on a human supervisor (Human-in-the-Loop - HITL) to adapt to adverse obstacles, sudden layout modifications, or partial hardware failures. However, existing HITL strategies frequently leave operators struggling with large volumes of data that demand real-time interpretation. To mitigate these challenges, we propose an agentic AI approach that integrates long-term memory with adaptive reasoning techniques, thereby reducing operator workload and minimizing disruptions in dynamic autonomous robotics operations. The proposed system incorporates hierarchical subagents to systematically integrate historical data, sensor logs, and iterative problem-solving techniques to address frequent challenges in multi-robot deployments, including localization failures, hardware malfunctions, and crowd-induced obstacles. Experimental evaluations comparing memory-augmented and baseline (no-memory) conditions reveal that its usage consistently yields higher solution accuracy and operator satisfaction. In particular, memory retrieval accelerates the resolution of recurring failure modes, while adaptive reasoning enhances real-time decision-making in novel or crowded scenarios. Text-based similarity metrics (Token Overlap and Semantic Alignment) further demonstrate that reusing verified domain language and strategies improves the clarity and maintainability of the recommended actions. The results underscore the viability of a modular, language-based system that combines data-driven diagnostics, robust memory mechanisms, and self-reflective planning for large-scale robot supervision. By uniting flexible LLM capabilities with HITL workflows, our proposal holds considerable promise for improving both efficiency and transparency in real-world autonomous robotics operations.