Active Inference in Contextual Multi-Armed Bandits for Autonomous Robotic Exploration

Autonomous selection of optimal options for data collection from multiple alternatives is challenging in uncertain environments. When secondary information about options is accessible, such problems can be framed as contextual multiarmed bandits (CMABs). Neuroinspired active inference (AIF) has gained interest for its ability to balance exploration and exploitation using the expected free energy objective function. Unlike previous studies that showed the effectiveness of AIF-based strategy for CMABs using synthetic data, this study aims to apply AIF to realistic scenarios, using a simulated mineralogical survey site selection problem. Hyperspectral data from the next generation airborne visible–infrared imaging spectrometer at Cuprite, Nevada, serves as contextual information for predicting outcome probabilities, while geologists’ mineral labels represent outcomes. Monte Carlo simulations assess the robustness of AIF against changing expert preferences. Results show AIF requires fewer iterations than standard bandit approaches with real-world noisy and biased data, and performs better when outcome preferences vary online by adapting the selection strategy to align with expert shifts.