Venue: CoRL 2023 Conference

Reviews:

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: The authors present a hierarchical approach to manipulating rope into a desired goal configuration. The hierarchy is broken down into a high-level solver with a discrete representation of the rope and a lower-level that uses a neural network to plan a sequence of robot actions between subgoals dictated by the high-level solver. The authors present several simulated experiments that investigate their approach against some simple baselines. 
review: Strengths:

- The authors present a novel approach to rope manipulation that is compelling.

Weaknesses

- The experiments the authors have chosen to highlight their proposed method and the research questions they proposed seem disconnected. 
- The authors do not validate their method against a competitive state-of-the-art background. I would suggest applying a hierarchical RL method. 
- The presentation of the results within the paper is poorly executed. A table would help a lot. Figure 3 is not referenced in the correct places and is not intuitive to follow (i.e. what is the y-axis).
- The paper needs to be thoroughly edited to rectify writing issues.
- Sim-to-real is a significant overheard for transferring this method to a real-world environment although the authors allude to "real-world applications" in their sensitivity analysis as if overcoming small changes in friction make it likely that this will naively transfer. 
- The format of the related work induces a lot of repetition.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The authors propose a compelling method to complete rope manipulation tasks. However, the clarity of presentation and the choice of experiments along with baselines needs to be addressed for the paper to show the advantages of their approach over other competitive methods.
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_of_paper: This paper proposes a pipeline for making it possible for a robot to tie knots and manipulate wires without relying on a large set of human demonstrations. This is done by splitting the approach into (i) a low-level part which involves learning how to represent states of the rope from perceptual data, and (ii) a high-level part that uses ideas from knot theory to reason about how the rope needs to be manipulated to bring it to the desired state. The approach is shown to generalize, demonstrating the ability to tie knots that were not seen during training. In total, the approach yields a system capable of handling knots in a category the authors label "easy" - a significant improvement on baselines such as deep RL, which can't even do that - in my view, fantastic progress.
review: This is an important problem. Enabling robots to manipulate ropes and wires at the same level as a human being would be very valuable commercially, and would greatly increase our understanding of how to solve more general dextrous manipulation problems. This is particularly interesting because, unlike many manipulation problems involving rigid physics, ropes present a continuum of degrees of freedom, and appropriate understanding needs to be developed to handle this.

I am not familiar with knot theory - and, I suspect, most readers will not be as well. Therefore, a more comprehensive review would have been helpful, in my opinion the background is too short. For example, the authors introduce the notion of "Reidemeister moves" which is an important notion because each such move represents steps in the high-level planner, but don't attempt to offer an explanation. At least something needs to be said about what those moves represent intuitively and why they are the appropriate knot-theoretic notion to use for high-level planning steps. If the authors think this is too complex to define properly - and I am skeptical this is the case - they should instead explain why this is too complex.

The writing is wordy, which makes certain sections feel like they take up a lot of space without containing a lot of content. The "prior work" section is arguably the worst offender. There is no need to repeatedly explain why the other papers in the literature are different from this one in point-by-point detail - this is a paper, not a review rebuttal. If this kind of detail is offered at all, this should be presented in the conclusion section after the reader has read about the paper's techniques. This section should therefore be cut to 1/3 its current length and the extra space used to better explain the background so that readers have the tools they need to understand the differences between this paper and other papers on their own. 
* For example, the "knot un-typing paragraph" could be replaced with "[these authors] have examined the task of un-tying a knot in a given rope, with most approaches operating using high-level abstractions which mirror the ones we study". There is no need to say that bringing a rope to an arbitrary state - rather than to an untied state - is more complex, as this is obvious.

Section 4 could have a more informative title than "Method", which instead describes something about how the method works.

Section 4 focuses on the steps taken by the planning algorithm, which is a tree-based algorithm where nodes are added based on picking a random node and executing random actions. One could easily generalize this to more general tree search algorithms, which might be faster at the cost of being more complex or more difficult to implement. It would be nice to formulate this in a general way, and say that a particular way of searching the tree is used by the authors because it works.

The data collection scheme is not described, but is critical to the algorithm's ultimate success. This part is therefore important enough that it needs to be described in the main body, not the appendix.

Figure 3 fonts are too small, and because my eyesight is not great I cannot tell what algorithms are doing well and which ones are not. Please make this figure significantly larger, so that its fonts are at least the same size as the paper's text.

In the text, "Figure 3" is not capitalized consistently. Please make this consistent.

The CoRL-required "limitations" section is not used effectively, because the authors use it to describe limitations a second time that have already been stated earlier in the text. As mentioned earlier, this paper would be significantly better if more space was dedicated to describing the background and methods in more detail rather than moving critical information into the appendix, which means that repeating information here harms the paper. Please consider moving the limitations to a subsection of the methods section, and consider moving other parts of the text that describe limitations elsewhere into this section.
* Additionally, the most important limitation of TWISTED is that - while a better algorithm than baselines - it also does not solve in the medium or hard levels. This should be mentioned in limitations, so that readers who don't read the rest of the paper can quickly see that this is the level of performance to be expected. Note that being upfront about this limitation does not devalue the work, since knot-tying is an important enough problem that even limited performance improvements are of good scientific value.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This paper brings great improvements on a hard problem, using an approach with a significant amount of bells and whistles which are largely justified and make sense. I am happy with the results and evaluations. The main downside is that the paper does not describe enough detail about the setting or method in the main body, due to a combination of repetitive text, too much detail about prior work, and generally inefficient use of space. The paper would be significantly improved with a a rework of the writing, but I think is already interesting enough that I am more than happy to see it published now in spite of its flaws.
* Since important parts of the algorithm such as the data-collection scheme of Section 4.5 are not described at all in the main text, it's also possible there are hidden problems that should have been mentioned as limitations have been withheld. I didn't detect such issues, but am very curious if other referees might have.
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_of_paper: This paper presents TWISTED, a pipeline for tying knots without demonstration through hierarchical planning. The high-level planner plans a path with breadth first search in the space of topological states, and the low-level planner uses a learned auto-regressive model to transition between topological states. The proposed method is evaluated in simulation using Mujoco and outperformed the baselines. TWISTED was tested on different friction coefficient and yielded similar results.
review: The main idea of this paper is interesting and the method is supported with empirical results. Admittedly, I am not familiar enough with the rope tying literature to know if something similar has been attempted before. 

The execution of the paper should be improved.  There should be more empirical data especially since this paper is simulation only. The presentation of the paper is unsatisfactory. Please refer to the “Questions for rebuttal” section for more details.

Strengths:

1.	Clear proposed idea

2.	Favorable empirical results

Weaknesses:

1.	Limited empirical data

2.	Unpolished presentation

3.	No hardware experiments
robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, I think the paper is quite interesting and the proposed idea could have impact. I am not familiar enough with rope manipulation to judge exactly how this paper stands relative to the literature, but it seems like a decent advancement. This could make a solid paper with improvements in execution and the presentation issues ironed out. However, in its current form, I cannot recommend it for CoRL.
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_of_paper: The paper propose a hierarchical planning algorithm for knot tying, which is built on knot-theory and doesn't require access to human demonstrations. The proposed algorithm first discretize the continuous state space of rope into topological states, and plan in this discretized space using graph search. To move between the configurations in the high-level plan, the authors utilize a learned inverse model, which predicts pick position and motion autoregressively. The authors show in simulation that the proposed method outperform various baselines.
review: Strengths
- Knot tying is a very challenging task and the results shown in the video are truly impressive. The paper definitely makes a step forward in the field of rope manipulation.
- The proposed hierarchical planning method seems really promising. It transforms the high-dimensional state space of rope into a fixed set of topological states based on the know theory, which significantly improves the efficiency of planning. The low-level inverse model is trained in a self-supervised manner and also works very well in tracking the high-level plan.
- The paper conducts thorough experiments to evaluate the effects of different design choices. Although no physical experiments is conducted, the authors show in simulation that the proposed method is robust to different phyiscal properties.

Weaknesses
- The proposed method is only tested in simulation but not real world.
- The paper makes a lot of simplifications in terms of perceptions and controls. It assumes perfect state estimation, which is very difficult in real world due to self-oclcusion. Also, it uses a floating gripper that can move freely. While in real world, a robot arm will introduce additional topological constraints that makes certain actions infeasible. Sometimes, the robot may need to release the gripper and regrasp the rope from the other side.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, I think it's a good paper that makes solid contribution in rope manipulation. The authors leverage knot theory to design a hierarchical planning method that tackles a really challenging task robotic manipulation task, knot tying. Although no physical experiment is performed, I think it's understandable since the focus is mainly on the planning side. And I do not expect all challenges (perception, planning, controls) are solved in a single paper. Therefore, I recommend weak accept and will raise my rating if all questions are clarified.
review_title: null

Meta-review: The paper presents a hierarchical planning algorithm, TWISTED, for solving the challenging problem of knot tying in rope manipulation. The proposed approach leverages concepts from knot theory and introduces a novel methodology that does not rely on human demonstrations. The algorithm involves two levels of planning: a high-level planner that operates in a discretized space of topological states and a low-level planner that employs an auto-regressive model to transition between these states. The results of the algorithm are evaluated in simulation using the Mujoco platform and compared against various baselines.
Strengths: The paper offers a novel approach to addressing knot tying in rope manipulation through the introduction of a hierarchical planning algorithm. The concept of utilizing topological states to represent the rope's configurations is innovative and demonstrates an effective way to reduce the complexity of the problem. The proposed method exhibits improved performance in simulation, surpassing the performance of various baselines, which underscores the efficacy of the algorithm. The paper demonstrates a systematic approach to evaluating the algorithm by conducting experiments to evaluate the impact of different design choices on its performance.
Weaknesses: While the simulation results are promising, the lack of real-world experimentation is a limitation. It is acknowledged that real-world conditions would present additional challenges, such as perception and control issues, which need to be considered for practical deployment. Some simplifications in terms of perception and control are made, including assuming perfect state estimation and the use of a floating gripper. These assumptions may not hold in real-world scenarios and should be addressed in future work.
The reviewers have raised valid concerns and provided constructive feedback on various aspects of the paper. The authors' responses indicate a thorough understanding of the issues and plans to address them. The revisions made in response to the reviewers' feedback are documented and are aimed at enhancing the clarity, presentation, and understanding of the proposed algorithm. While the absence of real-world experiments and the impact of simplifying assumptions are acknowledged limitations, the novelty and potential of the proposed method make it a valuable contribution to the field of robotic manipulation.
Decision: Accept (Poster)