Title: Efficient Strategy Learning by Decoupling Searching and Pathfinding for Object Navigation Show Chinese title

Title: 基于解耦搜索和路径查找的对象导航高效策略学习

Abstract: Inspired by human-like behaviors for navigation: first searching to explore unknown areas before discovering the target, and then the pathfinding of moving towards the discovered target, recent studies design parallel submodules to achieve different functions in the searching and pathfinding stages, while ignoring the differences in reward signals between the two stages. As a result, these models often cannot be fully trained or are overfitting on training scenes. Another bottleneck that restricts agents from learning two-stage strategies is spatial perception ability, since the studies used generic visual encoders without considering the depth information of navigation scenes. To release the potential of the model on strategy learning, we propose the Two-Stage Reward Mechanism (TSRM) for object navigation that decouples the searching and pathfinding behaviours in an episode, enabling the agent to explore larger area in searching stage and seek the optimal path in pathfinding stage. Also, we propose a pretraining method Depth Enhanced Masked Autoencoders (DE-MAE) that enables agent to determine explored and unexplored areas during the searching stage, locate target object and plan paths during the pathfinding stage more accurately. In addition, we propose a new metric of Searching Success weighted by Searching Path Length (SSSPL) that assesses agent's searching ability and exploring efficiency. Finally, we evaluated our method on AI2-Thor and RoboTHOR extensively and demonstrated it can outperform the state-of-the-art (SOTA) methods in both the success rate and the navigation efficiency. Show Chinese abstract

Abstract: 受人类导航行为的启发：首先搜索以探索未知区域，然后在发现目标后进行路径规划，最近的研究设计了并行子模块以在搜索和路径规划阶段实现不同的功能，但忽略了这两个阶段奖励信号的差异。 因此，这些模型通常无法完全训练或在训练场景上过拟合。 限制代理学习两阶段策略的另一个瓶颈是空间感知能力，因为这些研究使用了通用的视觉编码器，而没有考虑导航场景的深度信息。 为了释放模型在策略学习方面的潜力，我们提出了对象导航的两阶段奖励机制（TSRM），该机制在一次试验中解耦了搜索和路径规划行为，使代理在搜索阶段能够探索更大的区域，并在路径规划阶段寻找最优路径。 此外，我们提出了一种预训练方法深度增强掩码自编码器（DE-MAE），使代理能够在搜索阶段确定已探索和未探索区域，在路径规划阶段更准确地定位目标物体并规划路径。 另外，我们提出了一种新的度量标准——按搜索路径长度加权的搜索成功率（SSSPL），用于评估代理的搜索能力和探索效率。 最后，我们在AI2-Thor和RoboTHOR上对我们的方法进行了广泛评估，并证明它在成功率和导航效率方面都能优于最先进的（SOTA）方法。