Venue: CoRL 2021 Conference

Reviews:

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper presents a new model-based RL approach for cloth manipulation, which involves learning a novel graph-neural-net based dynamics of point-clouds. The system appears to outperform prior model-free and model-based approaches, and shows impressive sim2real transfer. 
strengths_and_weaknesses: Strengths

- The paper proposes a viable solution to the Cloth manipulation problem, which is very hard because of partial observability, high configuration space, and underlying real-world soft-body dynamics. 

- Zero shot sim2real transfer is impressive.

Weaknesses

- I found the paper hard to read in certain sections particularly the details of GNNs and how exactly the dynamics is simulated.

- Not much analyses is provided on how sensitive the overall success is to the choice of architecture

- It is surprising that one step dynamics fitting works for this application since other model-based RL approaches crucially rely on multi-step losses or DAGGER like corrections.

- One baseline that would be interesting is blackbox policy optimization against FLEX cloth models directly.
 
summary_of_recommendation: Novel approach for model-based cloth manipulation that shows promising sim2real transfer and relies on a new way to use GNNs to model point cloud dynamics. Overall, the presentation of architectural details could be significantly improved.
issues: See weaknesses above.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: In this work, a method that learns a visible connectivity dynamics model is presented for learning the observable portion of the point cloud and uses that for cloth smoothing task. The results of the proposed method was compared with state-of-the-art model-based and model-free reinforcement learning methods, showing promising results. The experiments also showed that their method is able to generalise for different shapes of fabrics and demonstrated zero-short sim-to-real transfer. 
strengths_and_weaknesses: Strengths:
1. The main contribution of the paper is implementation of a particle-based dynamics model for visible portion of cloth point cloud (visible connectivity dynamics) to accomodate a partial visibility. The model learns dynamics using the network presented in [7].
2. The shape specific training is addressed by having a student model with partial observations learns to imitate a teacher model with full-state information.

Weaknesses:
1. The proposed model predicts the mesh of the fabric but it is not clear that how robot action is taken into consideration in the model. It is obvious that the robot motion, even small motions, will change the state of the fabric and as a result will change the mesh to be predicted. In line 11 of that algorithm, it is mentioned that the inputs to the dynamic network are: the current cloth points (after the pick points were moved), historical cloth points, picked point and set of collision and mesh edges. It is surprising that the robot action is not taken in the input as it will change the state of the fabric and it is also easy to obtain.
2. The presentation can be further improved by including more details of the presented method. For example, how is the distance threshold R determined? I believe the choice is important in determining the collision edges and R may be different for different cloth types and sizes.
summary_of_recommendation: As written in the Strengths and Weaknesses section, the paper proposes an interesting method for cloth smoothing. The proposed approach is leveraging a network presented in previous works, but implemented in a different application, which could advance the development of methods in robot cloth smoothing. The paper is well presented in general but the presentation can be further improved by including more details of the presented method. For example, how is the distance threshold R determined? 
issues: 1.	In Figure 3 there is no definition of error bars, please define their meaning (are these standard deviation, standard error or the percentil range described at the beginning of section 4.1.).
2.	The article is well organized and easy to read, however, in my opinion, the conclusion section could have been elaborated a bit more.
3.	The statement “for all shapes, we test on 40 initial configurations” (line 238) seems not fully clear for me, this could be rephrased. I believe authors mean that for each shape the experiment was run 40 times, each time with different initial configuration of the fabric (cloth or T-shirt).
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary: Authors proposed a particle-based model to enable manipulation with cloths. The proposed approach, VCD, is able to learn effective models of the cloth, and it works effectively under the condition of partial observability. The efficacy of the approach is validated both in simulation and on a real robot.
strengths_and_weaknesses: Strength:
A very interesting way to model the cloth with particles
A very nice way to use teacher-student model to learn a model for partial observability
Impressive results that the learned model can sim-to-real transfer

Weakness:
The approach is only tested on the smoothing task. It is unclear at this point how the approach performs on other tasks such as folding.
It is hard to specify specific goal configurations in the current formulation.
The current formulation can only consider a single deformable object at a time.

summary_of_recommendation: I find this paper very easy to follow even without personal experience working with deformable object manipulation. Authors also make their contribution clear in the writing. Experiments are very extensive and results are very impressive.
issues: - I mainly have one concern about the approach on real robots: In my understanding, the number of nodes are quite dense for VCD. In the experiment, is the graph generation robust enough to the depth sensor noise? I think authors did not mention this part in the paper. I would like to see the factor of depth sensor noise discussed to fully understand its strength and weakness in the real robot scenario.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary: This paper proposed a general and efficient cloth smoothing scheme using the predicted visible connectivity dynamics (VCD).
By representing the cloth shape as a graph structure using GNN with point cloud data as input, the authors can obtain a representation of the dynamics that is robust to the effects of visibility and self occlusion.
Although the control policy used seems to be simple, it is still able to achieve smoothing of multiple types of cloths efficiently.
strengths_and_weaknesses: Strengths:
- The effectiveness of the framework was clearly demonstrated through careful comparative experiments. 
- The contribution of each component in the framework was also verified by conducting ablation experiments, which increased the value of this paper.
- I think this paper is not on a new technology, but rather on a proper system integration for using GNNs for cloth shape prediction with reliable procedure.

Weaknesses:
- It is unclear how to optimize the design parameters that may be correlated with the size of the object, such as voxelization and node connection threshold.
- The control policy was simple and did not make full use of the high-level dynamics model obtained, and although it was faster than conventional methods, it was still not at a practical level.
- It is difficult to say that this paper is self-contained, since much of the information is referred to in appendices and the past papers.

summary_of_recommendation: The proposed VCD has been carefully implemented from the prediction method to verification, and is a significant contribution.
Although there are still some open problems, they are not within the scope of this paper and are well worthy of acceptance.
issues: - The proposed graph representation method seems to produce dense graphs, but is there any problem of computational cost? 
Also, isn't there a trade-off between computational cost and accuracy in terms of the degree of voxelization?
- As auxiliary purpose inspired by active sensing to the control strategy, is it possible to give the reward for behavior that increases the prediction accuracy of VCD?
- It seems to be a learning problem without accurate supervised signals.
I would like to know whether this learning problem is unstable due to noises and/or outliers from wrong annotations.
review_title: null

Meta-review: All reviewers were generally positive, and found the use of graph neural networks as a dynamics model for deformable objects, to be an interesting and novel contribution. Although there were no major weaknesses raised, in the rebuttal, authors should address the queries raised by the reviewers in order to maintain good review scores. In particular, I would like to hear the authors' opinions on how this method would work with tasks other than smoothing, such as how goal configurations would be specified more generally, as raised by Reviewer hhGm.

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The authors have provided an updated paper with new experiments, and have addressed many of the issues raised by the reviewers. This is a novel method for deformable object manipulation, with some good real-world results. All four reviewers recommend acceptance of the paper. Now that the authors have shown that the method works for folding as well as smoothing, I recommend updating the title of the paper.
Decision: Accept (Poster)