Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explore the relationships between objects. However, due to differences in visibility among objects, it is easy to generate biases in object attention. Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias. In particular, we use the DOA graph to perform unbiased adaptive object attention (UAOA) on the object features and unbiased adaptive image attention (UAIA) on the raw images, respectively. To distinguish features in different branches, a concise adaptive branch energy distribution (ABED) method is proposed. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate (SR), success weighted by path length (SPL) and success weighted by action efficiency (SAE), respectively.

本研究提出了一种有向对象注意力图来指导智能体在显式学习对象之间的注意力关系，从而减少对象注意力偏差，并利用该方法在AI2-Thor数据集上进行评估，相较于最先进的方法，本方法成功率、路径长度加权成功率和行动效率加权成功率分别增加了7.4％，8.1％和17.6％。

无偏置定向对象关注图用于对象导航