Stochastic Neural Physics Predictor
Recently, neural-network based forward dynamics models have been proposed that attempt to learn the dynamics of physical systems in a deterministic way. While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to plausible but different trajectories that diverge from the ground truth. A system that predicts distributions of the future physical states for long time horizons based on its uncertainty is thus a promising solution. In this work, we introduce a novel robust Monte Carlo sampling based graph-convolutional dropout method that allows us to sample multiple plausible trajectories for an initial state given a neural-network based forward dynamics predictor. By introducing a new shape preservation loss and training our dynamics model recurrently, we stabilize long-term predictions. We show that our model’s long-term forward dynamics prediction errors on complicated physical interactions of rigid and deformable objects of various shapes are significantly lower than existing strong baselines. Lastly, we demonstrate how generating multiple trajectories with our Monte Carlo dropout method can be used to train model-free reinforcement learning agents faster and to better solutions on simple manipulation tasks.
The authors describe a neural architecture with dropout randomizer with the aim of producing an ensemble of physical trajectories that seem plausible for a human eye. The question is relevant and interesting. The authors bring in two improvements to the HRN model by (Mrowca et al., 2018). They drop the hierarchy from the shape loss, which is decipeted in Figure 3, and provide a change in the recurrent training decipited in Figure 4. Figure 4 is only showing the modified training, not the initial HRN training, although Figure 3 contains both. For the sake of consitency and good read the Figure 4 should contain the original training image. The description of the system is very verbose, but a concrete description of the system is only in the references, which make the manuscript hard to read and it does not fit well in a conference where one cannot assume that the audience is not an expert of this particular subfield. As a remedy, I would suggest, a simple one-hieararcy level architecture with hyperparameters to show what is really happening. One could use the system in Figure 1 as a more thorough example. The results looks good and valuable, altough the images provided are quite small.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Stochastic Neural Physics Predictor
Recently, neural-network based forward dynamics models have been proposed that attempt to learn the dynamics of physical systems in a deterministic way. While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to plausible but different trajectories that diverge from the ground truth. A system that predicts distributions of the future physical states for long time horizons based on its uncertainty is thus a promising solution. In this work, we introduce a novel robust Monte Carlo sampling based graph-convolutional dropout method that allows us to sample multiple plausible trajectories for an initial state given a neural-network based forward dynamics predictor. By introducing a new shape preservation loss and training our dynamics model recurrently, we stabilize long-term predictions. We show that our model’s long-term forward dynamics prediction errors on complicated physical interactions of rigid and deformable objects of various shapes are significantly lower than existing strong baselines. Lastly, we demonstrate how generating multiple trajectories with our Monte Carlo dropout method can be used to train model-free reinforcement learning agents faster and to better solutions on simple manipulation tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors describe a neural architecture with dropout randomizer with the aim of producing an ensemble of physical trajectories that seem plausible for a human eye. The question is relevant and interesting. The authors bring in two improvements to the HRN model by (Mrowca et al., 2018). They drop the hierarchy from the shape loss, which is decipeted in Figure 3, and provide a change in the recurrent training decipited in Figure 4. Figure 4 is only showing the modified training, not the initial HRN training, although Figure 3 contains both. For the sake of consitency and good read the Figure 4 should contain the original training image. The description of the system is very verbose, but a concrete description of the system is only in the references, which make the manuscript hard to read and it does not fit well in a conference where one cannot assume that the audience is not an expert of this particular subfield. As a remedy, I would suggest, a simple one-hieararcy level architecture with hyperparameters to show what is really happening. One could use the system in Figure 1 as a more thorough example. The results looks good and valuable, altough the images provided are quite small.
assistant
The authors describe a neural architecture with dropout randomizer with the aim of producing an ensemble of physical trajectories that seem plausible for a human eye. The question is relevant and interesting. The authors bring in two improvements to the HRN model by (Mrowca et al., 2018). They drop the hierarchy from the shape loss, which is decipeted in Figure 3, and provide a change in the recurrent training decipited in Figure 4. Figure 4 is only showing the modified training, not the initial HRN training, although Figure 3 contains both. For the sake of consitency and good read the Figure 4 should contain the original training image. The description of the system is very verbose, but a concrete description of the system is only in the references, which make the manuscript hard to read and it does not fit well in a conference where one cannot assume that the audience is not an expert of this particular subfield. As a remedy, I would suggest, a simple one-hieararcy level architecture with hyperparameters to show what is really happening. One could use the system in Figure 1 as a more thorough example. The results looks good and valuable, altough the images provided are quite small.