Venue: NeurIPS 2022 Workshop nCSI

Reviews:

final_score: rating: 2: Good workshop paper, Accept
review_content: 
**Summary**

The paper proposes a two-level framework for the object rearrangement task. The proposed framework learns a factorized entity embedding ($h^k$) with two independent factors: the object identity ($z^k$) and the object state ($s^k$). The first level in the framework learns to identify the entities and the transitions. The second level learns to cluster the transitions and generate a transition graph. The empirical evaluation of the proposed approach on three domains demonstrates the superiority of the proposed approach over existing approaches.

**Strengths**
* The problem setting is interesting, the proposed idea is original, and the contribution is significant.
* Empirical evaluations are thorough and demonstrate the benefits.

**Weaknesses**
* The paper write-up has a lot of scope for improvement. Some key elements are not clear. I expand upon this in the next section
* The paper does not seem to address the question of how this work is relevant to the nCSI workshop.


**Comments and Questions**
* The key insight needs elaborate discussion "Our key insight is that these two problems are both different instances of the same fundamental problem of partitioning a space into discrete groups, but at different levels of abstraction: learning how to cluster visual features into entities (Fig. 1b) and learning how to cluster transitions over entity-sets into transitions over". 
* As the paper builds up on SLATE architecture, a brief overview of SLATE should be provided in the main paper (not the appendix).
* Figure 3 is not clear. What is the subfigure 3b align, 3c isolate and 3d bind representing? the subfigure boundaries are also not clear.
* Some abbrevations are used in the paper without formal introduction. For example, CEM, MPC, MGS. They are only introduced in the Appendix.



review_title: Review for the Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement 

final_score: rating: 2: Good workshop paper, Accept
review_content: This paper aims to combine slot attention with sparse state transition reasoning. It is motivated from the task of object rearrangement, where not all actions cause a change in all objects. The method is split into two steps, where first a slot-based system is trained to identify the objects, and then a cluster procedure is used to identify the state transition graph. 

The setup and method is interesting and worth presenting at the workshop. However, the presentation of the paper could be improved, since it currently is very dense in the 5 pages and a few parts are unclear:
* Figure 1 has tiny fonts, that are not readable when printed. Please increase the font size and simplify the figure. It currently takes several minutes to understand it.
* Line 78-79: It is not really clear what the difference between $\hat{h}_{t+1}$ and $h_{t+1}$ is.
* Line 80: The argument of why $\hat{h}_0$ being independent enforces independence in general is unclear. Where is $\hat{h}_0$ used, if you train on pairs of $o_1,a_1\to o_2$? Why would the model even care about the initial state, if the intermediate distributions have completely different parameters than the initial state?
* The experiment section has too many figures. I recommend moving Figure 3 to the appendix. Further, the font is again very tiny in Figure 3 and the colorbar has too many ticks, which is unnecessary. Currently, it is hard to really understand the experiment section from jus tthe small paragraph (l. 172-179).
* The references have a few double citations, e.g. [23] and [24] are the same paper, [31] and [32]

Typos:
* Line 76: citation style incorrect (Chang et al. [6])
* Figure 4 caption: '[d]ataset'
review_title: Interesting work on object transition graphs

Meta-review: metareview: The paper is interesting but the relation to causality is unclear and needs to be expanded upon. The paper needs to be improved w.r.t the writing as both the reviewers have clearly pointed out. 
confidence: 3: The AC is confident that the evaluation is correct
Decision: 2: Good workshop paper, Accept