Paper: Memory-efficient particle filter recurrent neural network for object localization (ICLR-2024)

Reviewer comment (Novelty):
Unfortunately, there doesn’t appear to be much technical novelty of the proposed mePFRNN compared with PFRNN. The main difference is that PFRNN encodes the entire environment as a 2D grid, while mePFRNN does not require this, and operates on motions and states directly. I feel this could be a simple model variant rather than a novel technical contribution that warrants a separate publication.

Author response:
We kindly disagree that the modification of PFRNN is technical since the proper specification of the general-purpose PFRNN to the object localization problem is not obvious. The processing data from beacons and positions is not straightforward and requires additional tests that are presented in our study. Moreover, we perform an extensive comparison of GRU-based RNNs with classical filtering methods to demonstrate the trade-off between model size and resulting performance.

Reviewer comment (Experiments):
The method is evaluated in quite simple grid world environments, in a narrow setting with beacons placed in the environment. The method would be more convincing if applied to larger-scale data or without specific observations relating to beacons or obstacles.

Author response:
We selected the test environments such that they are challenging for the classical non-parametric particle filter method. Although the considered environments are grid worlds, they are not simple for filtering methods since in the case of the unknown initial state, a filtering method may confuse the symmetric blocks of the grid and fail to recover the correct state of the object. 
Our study considers a particular filtering problem stated formally in eq (6). We agree that other problem statements related to the filtering problem are possible, however, in this study, we focus on the particular form of the filtering problem and compare the methods in the corresponding setup.

Reviewer comment (Writing):
I found the paper quite hard to follow in places. For the introduction, a more linear narrative through each paragraph would be helpful. Eg. first paragraph on the problem to be solved, the second one on existing methods, and third one on the proposed approach leading into the contributions.

Author response:
We updated the citation style and revised the introduction section according to the above suggestions.

Reviewer comment (Presentation):
A lot of the citations need to be fixed to use \citep{}; many of them blend into the rest of the text without parentheses.

Author response:
We updated the citation style and revised the introduction section according to the above suggestions.

Reviewer comment (Experiments):
Consider the case of real world robot localization. How large would the memory requirement be? Table 1 suggests that the memory requirement for PFRNN is not very much with fewer particles. How much gain would the proposed method achieve when scaling up to a larger problem compared to PFRNN?

Author response:
In our work, we show that the number of trainable parameters in the mePFRNN model does not depend on the environment size in contrast to the previously proposed PFRNN model. We demonstrate this in challenging symmetric environments, where we can control the number of beacons, their locations, and also the map of the obstacles. Regarding real-world robot localization, we consider as one of the main directions for future work since currently it is hard to find the proper simulator that generates such environments.

The key feature of the proposed mePFRNN model is that its number of parameters does not depend on the environment size in contrast to the baseline PFRNN model. Thus, the larger problems can be solved with the mePFRNN model, which has the same number of trainable parameters.