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.

翻译：为推进自主机器人领域发展，特别是针对未知环境中的物体搜索任务，我们提出了一种以可能物体位置（POLo）评分为核心的新型框架。通过利用3D物体概率地图，POLo评分使智能体能做出数据驱动的决策，实现高效物体搜索。我们进一步引入POLoNet——一种经训练可近似计算高计算复杂度POLo评分的神经网络——来增强框架的实用性。我们的方法解决了端到端强化学习方法（在长时域任务中存在记忆衰减问题）和传统基于地图方法（忽略可见性约束）的关键局限。基于OVMM 2023挑战赛第一阶段的实验表明，配备POLoNet的智能体显著优于包括端到端强化学习技术和先验地图策略在内的多种基线方法。为进行综合评估，我们引入了新的性能指标，这些指标能揭示不同智能体在物体目标导航中的效率与效能。