Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency. Classical probabilistic approaches explicitly represent uncertainty but typically rely on handcrafted action-selection heuristics, while deep reinforcement learning enables adaptive policies but often suffers from slow convergence and limited interpretability. This paper proposes a hybrid object-search framework that integrates Bayesian inference with deep reinforcement learning. The method maintains a spatial belief map over target locations, updated online through Bayesian inference from calibrated object detections, and trains a reinforcement learning policy to select navigation actions directly from this probabilistic representation. The approach is evaluated in realistic indoor simulation using Habitat 3.0 and compared against developed baseline strategies. Across two indoor environments, the proposed method improves success rate while reducing search effort. Overall, the results support the value of combining Bayesian belief estimation with learned action selection to achieve more efficient and reliable objectsearch behavior under partial observability.

翻译：自主目标搜索对室内环境中的移动机器人构成挑战，原因在于部分可观测性、感知不确定性以及探索与导航效率之间的权衡需求。经典概率方法虽能显式表示不确定性，但通常依赖手工设计的动作选择启发式策略；而深度强化学习虽能实现自适应策略，却常面临收敛缓慢和可解释性有限的问题。本文提出一种融合贝叶斯推理与深度强化学习的混合目标搜索框架。该方法维护目标位置的空间置信地图，通过基于标定目标检测结果的贝叶斯推理实现在线更新，并训练强化学习策略直接从该概率表征中选取导航动作。在Habitat 3.0平台上的逼真室内仿真环境中，本方法经与所开发的基线策略对比评估后发现：在两种室内场景下，所提方法在减少搜索代价的同时提升了成功率。总体而言，实验结果证明了将贝叶斯置信估计与学习型动作选择相结合，可在部分可观测条件下实现更高效可靠的目标搜索行为。