  To advance the field of autonomous robotics, particularly in object search
tasks within unexplored environments, we introduce a novel framework centered
around the Probable Object Location (POLo) score. Utilizing a 3D object
probability map, the POLo score allows the agent to make data-driven decisions
for efficient object search. We further enhance the framework's practicality by
introducing POLoNet, a neural network trained to approximate the
computationally intensive POLo score. Our approach addresses critical
limitations of both end-to-end reinforcement learning methods, which suffer
from memory decay over long-horizon tasks, and traditional map-based methods
that neglect visibility constraints. Our experiments, involving the first phase
of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet
significantly outperforms a range of baseline methods, including end-to-end RL
techniques and prior map-based strategies. To provide a comprehensive
evaluation, we introduce new performance metrics that offer insights into the
efficiency and effectiveness of various agents in object goal navigation.
