Venue: ICLR 2022 Workshop OSC

Reviews:

final_score: rating: 2: Good workshop paper, accept
review_content: Summary: The paper proposes a way to bind action inputs with object vectors in object-centric dynamics models. While previous models simply broadcast the action input to all the object vectors -- thus all objects receive the action input equally, the proposed approach seeks to use an attention mechanism to decide which object vector should receive the action input.

Pros: The paper highlights the importance of finding the right architecture to make the action input interact with the state representations in dynamics models. The performance improvement reported with and without the attention mechanism is significant.

Cons: There seems to be a dilemma between soft and hard attention -- the soft attention is more expressive, however,  it also under-performs hard attention in some experiments. 

Summary: I vote in favor of accept.
review_title: Review

final_score: rating: 3: Strong accept
review_content: This paper presents an empirical investigation on the role of different binding mechanisms between actions and objects for world models that are grounded in object-based environments. Specifically, the authors investigate the benefit of two proposed binding mechanisms (soft and hard attention binding) across an extensive set of experiments, ranging from simple toy environments to a more challenging robotic manipulation task.

- I highly appreciate this work and consider it an important contribution towards a better understanding of how to process and model actions in object-based world models.
- The results and conclusions are very interesting: On the one hand side, they show a strong benefit of using hard attention over soft attention and the baseline in the 2D SHAPES and 3D CUBES environment. On the other hand, a dedicated attention-based binding mechanism does not always seem to be beneficial as is shown in the Atari environments. The authors attribute this to the fact that actions typically only directly affect one object in these environments while every slot captures information about all objects. I do not necessarily think that this is a “negative outcome” but was very pleased to see an open discussion about the reasons and interpretation of this result.
- The paper is very well written, providing all relevant details and thereby easy to follow.
- The experiments are very extensive with all results being presented and analyzed in a succinct and technical sound way. 

I strongly recommend this paper for acceptance and think it is a great thematic fit for this workshop!
review_title: Important contribution on how to bind actions to objects in contrastive world models

Meta-review: The reviewers agree the paper should be accepted at the workshop. Congratulations!

The authors are encouraged to address the 'soft-hard dilemma' raised by reviewer G4Nk when preparing the camera-ready version.
Decision: Accept (Poster)