Enhancing the Robustness of AI-Driven Robotic RFID Inventory Management Using Conformal Prediction

In this work, we present a novel approach to enhance the robustness of autonomous robotic Radio Frequency Identification (RFID) inventory systems using Conformal Prediction (CP). Recent AI-driven approaches, especially deep-learning models, have made significant advances in performing inventory strategies and action planning. However, these models lack the capability to measure uncertainty during the prediction process, which can result in accumulated errors and lead to catastrophic failures. To address the above challenge, we propose a confidenceguaranteed policy using CP to ensure reliable predictions in RFID inventory tasks. Our method focuses on managing the uncertainty in sub-goal estimation for a trained model, ensuring that predictions can meet or exceed a user-specific confidence level. We conduct extensive experiments to assess the proposed method by regulating an existing model and evaluate its effectiveness in identifying uncertain predictions. The experimental results demonstrate the effectiveness of our approach in improving both the reliability and efficiency of RFID inventory tasks, ensuring consistent and trustworthy operation.