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README.md

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 ## Problem Statement
 
-In the realm of object manipulation, human engagement typically manifests through a constrained array of discrete maneuvers. This interaction can often characterized by a handful of low-dimensional latent actions, such as the act of opening and closing a drawer. Notice that such interaction could diverge on different types of objects but the interaction mode such as opening and closing is discrete. In this paper, we explore how the learned prior emulates this limited repertoire of interactions and if such a prior can be learned from unsupervised play-data. we take a perspective that decomposes the policy into tw
+In the realm of object manipulation, human engagement typically manifests through a constrained array of discrete maneuvers. This interaction can often characterized by a handful of low-dimensional latent actions, such as the act of opening and closing a drawer. Notice that such interaction could diverge on different types of objects but the interaction mode such as opening and closing is discrete. In this paper, we explore how the learned prior emulates this limited repertoire of interactions and if such a prior can be learned from unsupervised play data. we take a perspective that decomposes the policy into two distinct components: a mode selector and a low-level action predictor, where the mode selector operates within a discretely structured latent space. Together, our method **ActAIM2** builds a prior of robotic interaction modes using discrete representation learning. 
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 ### sample object data collection
 
 run