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: In this work, a goal-conditioned cloth manipulation policy is developed that uses optical flow to improve its performance. Two networks are used: the first network estimates flow between observation and goal images; the second network use flow from the first network to predict pick points and place points. The proposed method was tested both in simulation and on a real robot setup, compared against other model-free and model-based cloth manipulation policies, with promising results shown in the experiments. 


strengths_and_weaknesses: The main contribution of the paper is the presentation of a flow-based approach for learning goal-conditioned cloth manipulation policies. As mentioned by the authors a common approach for goal-conditioned policy learning requires the network to learn the relationship between observation and the goal and simultaneously to use this relationship to reason over the actions. With their method, these two tasks are separated and the relationship task is performed using the flow network (but this could be better justified, see more comments below).
summary_of_recommendation: The paper presents an interesting idea for cloth manipulation (cloth folding in this work) by taking optical flow as the input to predict the pick and place points in the manipulation. It is claimed that the proposed method is better predicting the pick and place points compared to the ones having the observation learning and action prediction together. However, this is not well justified as essentially the proposed method can be extended to connecting the Flow network and PickNet together, i.e., learning the relationship between observation and the goal first and then predicting the actions in one network, trained in an end-to-end manner.
issues: 1. In this work, the relationship between observation and the goal and the actions predictions are done separately, how will it affect the performance if two tasks are performed in a network?
2. The ablations: I was not able to find (later found that in appendix) the information on what type of data these experiments were conducted: “one-step”, “multi-step” or “all”; and also how many repetitions were done to achieve the presented results (in table 2). This can be found in Appendix, however, this seems relevant enough to add in the main text shortly.
3. For the real world experiments, how many trials were conducted?
4. Table 1 presents the average value over several experiments conducted, It would be additionally informative to show a standard deviation for each average value. 
5. How was alpha taken as 30 for all experiments? For fabrics of different sizes, alpha is to be different, isn't it?
6.	The article is well organized and easy to read, however, in my opinion, the conclusion section could have been elaborated a bit more.
7.	Line 194, I suggest rephrasing the statement, instead “learned in Sec. 3.3” could be “as explained in Sec. 3.3” or “learned as explained in Sec.3.3”.
8.	PickNet – policy network, is composed of two sub-components PickNet1 and PickNet2, this naming convention can be a bit confusing at first glance. This is well explained in the text but without a proper read the PickNet1 and 2 can be considered as some variants of PickNet.
9.	Some information in the paper seems unnecessarily repeated: 
a.	Previous methods for goal-conditioning policy have to learn current and goal relationship and reasoning, while the presented method separates these two tasks – this information appears in lines: 126-129, 174-175, and shortly in conclusions (323).
b.	The improvement compared to the previous works – lines: 73, 87, 95, 142. (the context in these usages is sometimes a bit different but it still seems like repeating).
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: The paper contributes a novel and efficient framework for bimanual cloth manipulation. Specifically, the proposed method leverages the concept of optical flow to represent both the cloth pose and actions. Without relying of expert demonstrations or explicit reward signals, the proposed approach is shown to be capable of learning cloth manipulation policies that generalize to new cloth shapes without additional training.
strengths_and_weaknesses: **Strengths**
+ The paper addresses a timely and well-motivated problem.
+ The use of optical flow for cloth manipulation seems to be a novel and ingenious application of well understood concept from a different filed (computer vision / estimation).
+ The experimental design includes multiple tasks (including those that require multiple steps), ablation experiments, a real-robot platform, and comparisons against SOTA approaches to cloth manipulation. Further, the results clearly demonstrate the utility and relative benefits of the proposed approach over existing approaches.
+ The method neither relies on expert demonstrations nor explicit reward signal. It simply leverages random explorations to collect data for supervised learning of flow prediction
+ The method autonomously switches between single and dual arm behavior depending on the distance between pick (or place) points.

**Weaknesses**

- The related work section on "Policy Learning for Cloth Manipulation" merely lists model-based and model-free methods for cloth manipulation, and mentioned that experimental comparisons are carried out. A qualitative discussion on the similarities and differences (much like how it is done in other subsections of related work) would be a lot more useful here.
- The approach assumes that the appropriate number of sub goals required for any given goal is known and each of those sub-goals provided to the robot.
- The approach relies on a simulator for ground-truth. While this reliance is not a weakness in itself, the paper needs to make this more important. For instance, this reliance must be disclosed in the introduction.
- The paper does not cite or utilize an important related work (Garcia-Camacho et al., RAL, 2020) that contributes a comprehensive collection of evaluation metrics and benchmark scenarios for bimanual cloth folding that would have made the evaluation much stronger. Further, this prior somewhat weakness the second contribution of the paper relating to the test suite for benchmarking

**References**

- Garcia-Camacho, Irene, et al. "Benchmarking bimanual cloth manipulation." IEEE Robotics and Automation Letters 5.2 (2020): 1111-1118.
summary_of_recommendation: The paper proposes a novel and effective approach to learning bimanual cloth manipulation policies. Barring some relatively minor weaknesses in the experimental evaluation, the proposed method appears to be much more efficient and somewhat more effective than SOTA approaches.
issues: 1. Please considering removing the last bullet from the list of contributions as evaluation of a proposed framework can not considered as a contribution. Having said that, it might be appropriate to list the ability of the proposed framework to general to other cloth colors and shapes as a contribution.
2. How robust is the proposed approach expected to be variations in the background?
3. It seems difficult to know how many sub-goals are necessary for a given task/goal. Can exiting methods (e.g., Ref. [33]) address this challenge? 
4. For the ablation experiments, please include the error associated with the full method in Table 2 (currently, only relative improvements are reported in text and the actual error is only included in the supplementary material).
5. I could not quite understand the motivation for not including key information in Table S2 from the appendix in the manuscript (e.g., many variants of the Fabric-VSF baseline from this table do better than what is reported in Table I for Fabric-VSF). Why are the best performing variants of the baseline not included in the manuscript? 
6. Unlike the proposed approach and Fabric-VSF, why was Lee et al.'s ability to generalize to new cloth shapes not evaluated?
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: The authors proposing using dense point-to-point cloth correspondence (flow) as a representation for cloth manipulation. The authors propose training a flow model in simulation which produces point correspondences between observations (initial configurations) and goal states of the cloth. From this representation they then predict pick points and use the predicted flow to implicitly determine placement locations, resulting in a final pick-and-place style policy. They show convincing results in a bimanual tabletop environment with two Panda arms.

Update (Post Author Response):
I appreciate the author's response to my questions; fundamentally my opinion of this paper still stands: It proposes an interesting and practical representation for cloth manipulation and they present fairly compelling evidence for its efficacy in the regime that it was designed for (fairly nicely behaved scenes and initial conditions). I think this paper is a useful addition to the field and advocate for its acceptance.
strengths_and_weaknesses: The main strengths of this work lie in its simplicity and effectiveness. The authors show that simple point-to-point correspondence, trained in sim, generalizes well to the real world and results in a usable system (even ~2cm of placement error in the multi-step version is pretty impressive compared to prior work). The authors also nicely ablate the differences between the flow-based input and the flow-based output parameterization. Interestingly (and surprisingly for me), most of the benefit came from the output parameterization using flow. I also found the paper to be well written and clearly presented, without unnecessary pomp and circumstance to try to dress up the work (at the cost of clarity and understandability). This is refreshing and great to see!

The biggest weakness of this paper was the lack of dynamics incorporation. Generally, these sorts of pick and place policies are inefficient compared to policies that incorporate dynamic effects (such as grasping and shaking a cloth to flatten it before the next fold). This isn't a fatal flaw, but it would strengthen the paper to briefly discuss this limitation and how a flow-based representation might fit into a dynamically aware paradigm in the future.

Another question is how well the long-term temporal correspondence works when dealing with highly crumpled initial conditions. By relying so heavily on good long-horizon flow/correspondence predictions, it seems that this method might struggle with such cases (e.g. a crumpled ball of a shirt). It would also strengthen the paper to evaluate such examples (even if the answer is "this representation works less well in those cases").
summary_of_recommendation: This paper is relatively novel (correspondence-based representations and flow have been around for quite a while, as the authors themselves note, but their application to cloth folding in this way is novel). In the relatively controlled domain the authors explore, this approach appears effective and the work is well-written and includes useful, and in some cases surprising (see my reaction to the ablation section) information for other practitioners.
issues: I have no major issues that I need addressed. In a vacuum (i.e. not having discussed with other reviewers) would advocate for acceptance in the paper's current state.
review_title: null

Meta-review: All three reviewers were generally positive about the paper, and the main weaknesses were based on small queries and clarifications, which should be addressed in the rebuttal. Along with these, I would also like the authors to particularly address two issues raised by the reviewers. Firstly, I would like to hear motivation for the need to separate the flow network and the pick network, rather than training a single end-to-end network, as raised by Reviewer a9Nv. Secondly, I would like to hear the authors' option on whether the need to manually specify the sub-goals for a particular task, as mentioned by Reviewer RKFg, is a significant weakness of the work.

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After the reviews, authors have provided an updated paper which addresses the reviewers' main issues, including some new experiments. This is a new state-of-the-art method for deformable object manipulation, with good real-world results, and all three reviewers recommend acceptance of the paper.
Decision: Accept (Poster)