论文标题
利用体现模拟以通过相互作用检测新对象类
Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction
论文作者
论文摘要
在本文中，我们提出了一种新的方法，用于幼稚的药物检测其相互作用中遇到的新物体。我们在给定已知对象类型的堆叠任务上训练加强学习政策，然后观察试图根据相同训练的策略堆叠各种其他对象的代理的结果。通过从对上述堆叠游戏结果训练的卷积神经网中提取嵌入向量，我们可以确定给定对象与已知对象类型的相似性，并确定给定对象是否可能与已知类型相似，以至于以至于被认为是新型对象。我们在使用两个不同策略收集的两个数据集上介绍了该方法的结果，并证明了代理商需要从其环境中提取哪些信息以做出这些新颖性判断。
In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe the results of the agent attempting to stack various other objects based on the same trained policy. By extracting embedding vectors from a convolutional neural net trained over the results of the aforementioned stacking play, we can determine the similarity of a given object to known object types, and determine if the given object is likely dissimilar enough to the known types to be considered a novel class of object. We present the results of this method on two datasets gathered using two different policies and demonstrate what information the agent needs to extract from its environment to make these novelty judgments.
