- 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.
Output: en