Update README.md

README.md

@@ -2,7 +2,27 @@
 
 ## 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 two distinct components: a skill selector and a low-level action predictor, where the skill selector is operating within a discretely structured latent space.
+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
+### sample object data collection
+
+run 
+
+```
+. collect_data.sh
+```
+
+to collect dataset. 
+
+### To access full object dataset from where2act
+
+## Training
+
+run
+
+```bash
+python scripts/train_aff.py
+```
+o distinct components: a skill selector and a low-level action predictor, where the skill selector is operating within a discretely structured latent space.
 
 We introduce **ActAIM2**, which given an RGBD image of an articulated object and a robot, identifies meaningful interaction modes like opening drawer and closing drawer. ActAIM2 represents the interaction modes as discrete clusters of embedding. ActAIM2 then trains a policy that takes cluster embedding as input and produces control actions for the corresponding interactions. 
 
@@ -98,7 +118,7 @@ https://github.com/pairlab/actaim2-eccv24/assets/30140814/f08ec7d0-687c-4331-96a
 
 https://github.com/pairlab/actaim2-eccv24/assets/30140814/cbf5b5db-e41b-4e59-9043-df8f54d22eda
 
-### Here are mode qualitative results of how robot interacting with different types of articulated objects
+### Here are more qualitative results of how robot interacting with different types of articulated objects
 
 Interacting with a switch and perform turning on and turning off