Confidence-Guided Escalation in Warehouse Robotics: A Human-in-the-Loop Reinforcement Learning Framework

Computer Engineering

As warehouse automation becomes increasingly dependent on autonomous robotic agents, ensuring safe and trustworthy decision-making under real-world uncertainty has become a critical challenge. Conventional reinforcement learning (RL) models though highly effective in simulation often falter in dynamic environments where visual ambiguity, sensor noise, and human interference are common. This paper proposes a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) for visual perception, a Proximal Policy Optimization (PPO) agent for control, and a Human-in-the-Loop (HITL) escalation framework guided by real-time confidence and policy entropy thresholds. When the system detects high uncertainty either from low visual classification confidence or elevated policy entropy it escalates the decision to a human supervisor, allowing for safe intervention without halting autonomy. This selective intervention reduces error rates while maintaining system efficiency. Experimental results in a simulated warehouse environment demonstrate a 60% reduction in false positives, with under 10% of decisions requiring human oversight. Furthermore, the architecture is designed with extensibility toward Explainable AI (XAI), offering a path for future systems to justify actions autonomously, replacing human input with machine-generated transparency. The proposed framework thus offers a scalable, ethically aligned approach to safe reinforcement learning in high-stakes autonomous robotics.

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