用贝叶斯推理与强化学习结合，让机器人更高效找物。
Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics
- 用贝叶斯更新目标位置信念图，实时融合检测结果
- 在Habitat 3.0中成功率更高，搜索路径更短
- 适合需要可靠导航的室内移动机器人场景
移动机器人在室内环境中自主寻物面临部分可观测、感知不确定性及探索与导航效率权衡的挑战。传统概率方法显式建模不确定性，但依赖手工设计的动作选择启发式；深度强化学习可生成自适应策略，但常收敛慢且难解释。本文提出一种融合贝叶斯推断与深度强化学习的混合寻物框架：维护目标位置的空间信念图，通过校准的物体检测在线更新，并训练强化学习策略直接从该概率表示中选择导航动作。在Habitat 3.0真实室内仿真环境中评估，对比多种基线策略，在两个不同室内环境上均提升成功率达12%以上，同时减少搜索努力。结果表明，结合贝叶斯信念估计与学习型动作选择，可在部分可观测条件下实现更高效可靠的寻物行为。
原文摘要 · Abstract (English)
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.
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