Venue: ICLR 2017 conference

Reviews:

final_score: rating: null
review_content: 1. Battaglia et al. (2016) and this work make counter claims on the ability to simulate over many timesteps very effectively when only trained for next-timestep prediction. Why did this work not include a quantitative comparison?
2. Why does this article choose to use compare only velocities and not positions of the balls?
3. Why does this work choose only to vary the shape and not the size of the scene configurations?
4. How does the work compare to Fragkiadaki et al. (2015) ? Can you provide an experimental comparision?
review_title: pre-review questions

final_score: rating: null
review_content: * What happens if the yellow pairwise part of the NPE model is replaced with a difference between two object representations? The yellow pairwise encoder increases the capacity of NPE compared to NP, so how important is that added capacity versus the pairwise factorization?

* Is the LSTM model supposed to be both non-pairwise and non-compositional? It does not take advantage of the pairwise factorization, but it is compositional (each hidden state is a function of the previous hidden state). Is a more specific notion of compositionality important? Would a non-compositional but pairwise lesion be possible?

* There seems to be a disconnect between the evaluation metrics in figure 3 and my intuitions about performance from watching the provided simulation GIFs. The gap between NPE and NP/LSTM curves in figure 3 is significant, but only small. On the other hand, it seems like NPE handles object collisions in a basically reasonable manner (balls bounce off each other and change directions) while NP/LSTM models fail utterly (balls are attracted to one another after collisions occur). Do the authors also observe this? If so, what are the evaluation metrics missing?

* Why is angular velocity predicted in addition to velocity if it is always 0?
review_title: Initial Questions

final_score: rating: null
review_content: * Neighborhood Mask

Neighborhood mask implicitly assumes certain minimum velocity for contextual objects. I wonder if authors have experimented with getting rid of the mask and letting model implicitly figure out which objects should be used for context? Or alternatively, having dynamic mask which is the function of relative distance and velocity between context and focus objects?

* Comparison to Fragkiadaki et al. (2015)

Fragkiadaki et al. (2015) does appear to be the closest published state-of-the-art (Battaglia et al. (2016) will technically not be published until next week at NIPS). Is there a reason why a more direct comparison with Fragkiadaki et al. (2015) is not carried out? This would strengthen the paper. 

* Error in Figure 2?

It appears that there maybe an arrow missing in Figure 2 (bottom, middle)?

* LSTM baseline

I understand the reasoning for perturbing the ordering of contextual objects. However, shouldn't there be a more natural ordering? e.g., rank order based on time to collision -- relative distance between contextual and focus object divided by their relative velocity. 

Thank you.
review_title: pre-review questions

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Summary
===
This paper proposes the Neural Physics Engine (NPE), a network architecture
which simulates object interactions. While NPE decides to explicitly represent
objects (rather than video frames), it incorporates knowledge of physics
almost exclusively through training data. It is tested in a toy domain with
bouncing 2d balls.

The proposed architecture processes each object in a scene one at a time.
Pairs of objects are embedded in a common space where the effect of the
objects on each other can be represented. These embeddings are summed
and combined with the focus object's state to predict the focus object's
change in velocity. Alternative baselines are presented which either
forego the pairwise embedding for a single object embedding or
encode a focus object's neighbors in a sequence of LSTM states.

NPE outperforms the baselines dramatically, showing the importance of
architecture choices in learning to do this object based simulation.
The model is tested in multiple ways. Ability to predict object trajectory
over long time spans is measured. Generalization to different numbers of objects
is measured. Generalization to slightly altered environments (difference
shaped walls) is measured. Finally, the NPE is also trained to predict
object mass using only interactions with other objects, where it also
outperforms baselines.


Comments
===

* I have one more clarifying question. Are the inputs to the blue box in
figure 3 (b)/(c) the concatenation of the summed embeddings and state vector
of object 3? Or is the input to the blue module some other combination of the
two vectors?


* Section 2.1 begins with "First, because physics does not
change across inertial frames, it suffices to separately predict the future state of each object conditioned
on the past states of itself and the other objects in its neighborhood, similar to Fragkiadaki
et al. (2015)."

I think this is an argument to forego the visual representation used by previous
work in favor of an object only representation. This would be more clear if there
were contrast with a visual representation.


* As addressed in the paper, this approach is novel, though less so after taking
into consideration the concurrent work of Battaglia et. al. in NIPS 2016 titled
"Interaction Networks for Learning about Objects, Relations and Physics."
This work offers a different network architecture and set of experiments, as
well as great presentation, but the use of an object based representation
for learning to predict physical behavior is shared.


Overall Evaluation
===

This paper was a pleasure to read and provided many experiments that offered
clear and interesting conclusions. It offers a novel approach (though
less so compared to the concurrent work of Battaglia et. al. 2016) which
represents a significant step forward in the current investigation of
intuitive physics.
review_title: Good work, and a pleasure to read

final_score: rating: 7: Good paper, accept
review_content: Paper proposes a neural physics engine (NPE). NPE provides a factorization of physical scene into composable object-based representations. NPE predicts a future state of the given object as a function composition of the pairwise interactions between itself and near-by objects. This has a nice physical interpretation of forces being additive. In the paper NPE is investigated in the context of 2D worlds with balls and obstacles. 

Overall the approach is interesting and has an interesting flavor of combining neural networks with basic properties of physics. Overall, it seems like it may lead to interesting and significant follow up work in the field. The concerns with the paper is mainly with evaluation, which in places appears to be weak (see below). 

> Significance & Originality:

The approach is interesting. While other methods have tried to build models that can deal with physical predictions, the idea of summing over pair-wise terms, to the best of my knowledge, is novel and much more in-line with the underlying principles of mechanics. As such, while relatively simple, it seems to be an important contribution. 

> Clarity:

The paper is generally well written. However, large portion of the early introduction is rather abstract and it is difficult to parse until one gets to 5th paragraph. I would suggest editing the early part of introduction to include more specifics about the approach or even examples ... to make text more tangible.

> Experiments

Generally there are two issues with experiments in my opinion: (1) the added indirect comparison with Fragkiadaki et al (2015) does not appears to be quantitatively flattering with respect to the proposed approach, and (2) quantitative experiments on the role the size of the mask has on performance should really be added. Authors mention that they observe that mask is helpful, but it is not clear how helpful or how sensitive the overall performance is to this parameter. This experiment should really be added.

I do feel that despite few mentioned shortcomings that would make the paper stronger, this is an interesting paper and should be published.
review_title: Review

final_score: rating: 6: Marginally above acceptance threshold
review_content: - summary

The paper proposes a differntiable Neural Physics Engine (NPE). The NPE consists of an encoder and a decoder function. The NPE takes as input the state of pairs of objects (within a neighbourhood of a focus object) at two previous time-steps in a scene. The encoder function summarizes the interaction of each pair of objects. The decoder then outputs the change in velocity of the focus object at the next time step. The NPE is evaluated on various environments containing bouncing balls.

- novelty

The differentiable NPE is a novel concept. However, concurrently Battaglia et al. (NIPS 2016) proposes a very similar model. Just as this work, Battaglia et al. (NIPS 2016) consider a model which consists of a encoder function (relation-centric) which encodes the interaction among a focus object and other objects in the scene and a decoder (relation-centric) function which considers the cumulative (encoded) effect of object interactions on the focus object and predicts effect of the interactions.  Aspects like only considering objects interactions within a neighbourhood (versus the complete object interaction graph in Battaglia et al.) based on euclideian distance  are novel to this work. However, the advantages (if any) of NPE versus the model of Battaglia et al. are not clear. Moreover, it is not clear how this neighbourhood thresholding scene would preform in case of n-ball systems, where gravitational forces of massive objects can be felt over large distances.

- citations 

This work includes all relevant citations.

- clarity

The article is well written and easy to understand.

- experiments 

Battaglia et al. evaluates on wider variety senerios compared to this work (e.g. n-bodies under gravitation, falling strings). Such experiments demonstrate the ability of the models to generalize. However, this work does include more in-depth experiments in case of bouncing balls compared to Battaglia et al. (e.g. mass estimation and varying world configurations with obstacles in the bouncing balls senerio). 

Moreover, an extensive comparison to Fragkiadaki et al. (2015) (in the bouncing balls senerios) is missing. The authors (referring to answer to question 4) do point out to comaprable numbers in both works, but the experimental settings are different.  Comparison in a billiard table senerio like that Fragkiadaki et al. (2015) where a initial force is applied to a ball, would have been enlightening. 

The authors only evaluate the error in velocity in the bouncing balls senerios. We understand that this model predicts only the velocity (refer to answer of question 2). Error analysis also with respect to ground truth ball position would be more enlightening. As small errors in velocity can quickly lead to entirely different scene configuration.

- conclusion / recommendation

The main issue with this work is the unclear novelty with respect to work of Battaglia et al. at NIPS'16. A quantitative and qualitative comparison with Battaglia et al. is lacking.  But the authors state that their work was developed independently.

Differentiable physics engines like NPE or that of Battaglia et al. (NIPS 2016) requires generation of an extensive amount of synthetic data to learn about the physics of a certain senerio. Moreover, extensive retraining is required to adapt to new sceneries (e.g. bouncing balls to n-body systems). Any practical advantage versus generating new code for a physics engine is not clear. Other "bottom-up" approaches like that of  Fragkiadaki et al. (2015) couple vision along with learning dynamics. However, they require very few input parameters (position, mass, current velocity, world configuration), as approximate parameter estimation can be done from the visual component.  Such approaches could be potentially more useful of a robot in "common-sense" everyday tasks (e.g. manipulation). Thus, overall potential applications of a differentiable physics engine like NPE is unclear.
review_title: A Compositional Object-Based Approach to Learning Physical Dynamics

Meta-review: The paper proposes a Neural Physics Engine for predicting intuitive physics. It is able to model the objects' dynamics and pairwise object interactions in order to build predictive models. This is a very nice direction, as also noted by the reviewers. One reviewer was particularly enthusiastic, while the other two less so. The main concerns were similarities to existing work, which however can be considered as done in parallel. The reviewers also had comments wrt evaluation, which the authors addressed. This is a good paper, and the AC recommends acceptance. The authors are also encouraged to look at "Learning Multiagent Communication with Backpropagation" by Sukhbaatar, whose method (albeit applied in a different context) seems relevant to the proposed approach.
Decision: Accept (Poster)