论文标题
在世界模型中对物体的约束作用
Binding Actions to Objects in World Models
论文作者
论文摘要
我们使用动作注意机制研究了与对象因素模型中对象结合作用的问题。我们提出了两种关注机制，用于将动作与对象，软关注和强烈注意的绑定，我们在五个环境的结构化世界模型的背景下进行了评估。我们的实验表明，艰苦的关注有助于对比训练有素的结构化世界模型，以学会在基于对象的网格世界环境中分离单个对象。此外，我们表明，软关注会提高经过机器人操纵任务训练的有因式的世界模型的性能。当关注关注环境中的受操纵对象时，学到的动作注意力权重可以用来解释有生物的世界模型。
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of structured world models for five environments. Our experiments show that hard attention helps contrastively-trained structured world models to learn to separate individual objects in an object-based grid-world environment. Further, we show that soft attention increases performance of factored world models trained on a robotic manipulation task. The learned action attention weights can be used to interpret the factored world model as the attention focuses on the manipulated object in the environment.
