Hierarchical object-to-zone graph for object navigation

物体导航任务是要求智能体在未知环境中找到指定的目标物体。先前的工作通常利用深度学习模型通过强化学习方式来训练智能体进行实时的动作预测。然而当目标物体没有出现在智能体的视野中，智能体往往由于缺乏指导而不能做出高效的动作。本文提出一种由物体到区域的多层级（hierarchical object-to-zone, HOZ）图，为智能体提供由粗到细的先验信息指导，并且HOZ图可以在新的环境中根据观测信息来不断更新。具体来说，HOZ图包含场景节点、区域节点和物体节点。借助于HOZ图，智能体可以根据目标物体以及当前观测规划出一条从当前区域到目标物体可能出现区域的路径。本文在AI2-Thor三维模型器中验证了所提出的方法，所选用的评测指标，除了常用的成功率（Success Rate, SR）和按路径长度加权的成功率（Success weighted by Path Length, SPL），本文还提出了用于评测导航中动作有效性的指标：按动作效率加权的成功率（Success weighted by Action Efficiency, SAE），实验结果证明了我们方法的有效性。

Abstract

The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an agent to predict actions in real-time. However, in the unseen environment, when the target object is not in egocentric view, the agent may not be able to make wise decisions due to the lack of guidance. In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments. In particular, the HOZ graph is composed of scene nodes, zone nodes and object nodes. With the pre-learned HOZ graph, the real-time observation and target goal, the agent can constantly plan an optimal path from zone to zone. In the estimated path, the next potential zone is regarded as sub-goal, which is also fed into the deep reinforcement learning model for action prediction. Our methods are evaluated on the AI2-Thor simulator. In addition to widely used evaluation metrics Success Rate (SR) and Success weighted by Path Length (SPL), we also propose a new evaluation of Success weighted by Action Efficiency (SAE) that focuses on the effective action rate. Experimental results demonstrate the effectiveness and efficiency of our proposed method.

• Sixian Zhang, Xinhang Song, Yubing Bai, Weijie Li, Yakui Chu, and Shuqiang Jiang. Hierarchical object-to-zone graph for object navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15130–15140, October 2021.