Object goal visual navigation is a challenging task that aims to guide a
robot to find the target object based on its visual observation, and the target
is limited to the classes pre-defined in the training stage. However, in real
households, there may exist numerous target classes that the robot needs to
deal with, and it is hard for all of these classes to be contained in the
training stage. To address this challenge, we study the zero-shot object goal
visual navigation task, which aims at guiding robots to find targets belonging
to novel classes without any training samples. To this end, we also propose a
novel zero-shot object navigation framework called semantic similarity network
(SSNet). Our framework use the detection results and the cosine similarity
between semantic word embeddings as input. Such type of input data has a weak
correlation with classes and thus our framework has the ability to generalize
the policy to novel classes. Extensive experiments on the AI2-THOR platform
show that our model outperforms the baseline models in the zero-shot object
navigation task, which proves the generalization ability of our model. Our code
is available at:
this https URL.

本文研究了零样本对象目标视觉导航任务，并提出了一种基于语义相似性网络的新型框架，用于在未训练过的类中引导机器人查找目标，通过在 AI2-THOR 平台上进行了广泛的实验，证明了我们模型的一般化能力强于基线模型。

零样本物体导向视觉导航

Zero-shot object goal visual navigation

This work focuses on object goal visual navigation, aiming at finding the
location of an object from a given class, where in each step the agent is
provided with an egocentric RGB image of the scene. We propose to learn the
agent's policy using a reinforcement learning algorithm. Our key contribution
is a novel attention probability model for visual navigation tasks. This
attention encodes semantic information about observed objects, as well as
spatial information about their place. This combination of the "what" and the
"where" allows the agent to navigate toward the sought-after object
effectively. The attention model is shown to improve the agent's policy and to
achieve state-of-the-art results on commonly-used datasets.

本研究对物体目标视觉导航进行了研究，旨在通过强化学习算法学习智能体的策略。我们提出了一种新的注意概率模型来改进代理策略，并在常用数据集上取得了最先进的结果。