Venue: CoRL 2022 Conference

Reviews:

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 introduces a method that leverages spatial abstraction and temporal abstraction to facilitate planning for long-horizon deformable object manipulation. Temporal abstraction is achieved using primitive skills and spatial abstraction is achieved by independently representing each object in a scene. More specifically, the proposed model consists of 1) a Point Cloud VAE that encodes the point cloud of each object into a latent vector and can be used to generate imagined subgoals, 2) a set point cloud policy for each skill that operates on a set of object point clouds, 3) a set feasibility predictor that operates on latent codes of objects to predict skill feasibility, and 4) a set reward predictor that predicts distance to the goal. A planning algorithm uses the learned components to predict a sequence of primitive skills and the latent subgoals for the skills. The latent subgoals are decoded with the VAE decoder and used to condition the set point cloud policy.

strengths_and_weaknesses: Strengths:
1. Compared to the most-related prior work [7], this paper introduces set-based versions of the skill policy, feasibility predictor, and reward predictor using clever designs of the networks, objective functions, and planning algorithm. 
2. A nice property of the planning algorithm is that it can work with skills that change the number of objects in a scene.
3. The attention mask is used to exclude irrelevant parts of the scene. The importance of this design is validated in Table 4 in the appendix.
4. The results presented in Table 1 are very strong. The Flat3D and DiffSkill-Image baselines help to demonstrate the importance of spatial abstraction, which is the main contribution of this paper.
5. It’s great to see that this method also generalizes well to the real robot using heuristic controllers for the skills.
6. The discussion and ablation study of the feasibility predictor also provide a lot of insights on how to ensure its gradient can successfully guide the search for correct subgoals.

Weakness:
1. It would be great to see a discussion of the complexity of the algorithm in the main text. Since the planning algorithm performs nested optimization at 3 different levels. I am curious if the algorithm can easily scale to tasks that require chaining more than 6 skills (e.g., CRS-Third, CRS-Fourth, and etc) and more than 3 objects. Will the algorithm work if there are distractor objects in the scene?
2. It would be interesting to test the generalization capability of the proposed method with more tasks. Can the method be evaluated on different combinations of the primitive skills (i.e., Lift, Spread, Gather, Transport, Cut, Rearrange).

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: This paper combines spatial and temporal abstraction to achieve long-horizon deformable manipulation. The proposed planning framework and neural network modules can deal with variable numbers of objects and compose primitive skills to manipulate more objects in test time. The experiment shows that this object-centric scene representation is more effective than existing methods that do not use spatial abstraction. The real robot experiment also validates the proposed method.
issues: Besides the questions I raised above, I have the following comments:
1. In Line 91, $T$ is used to denote the number of actions between the initial and goal. $T$ is used later to represent the skill horizon. Since multiple skills are executed between the initial step and the goal step, maybe a different symbol should be used.
2. In Line 92, $i$ is used to index time but it is later used to index objects.
3. Line 117 is confusing since it states that the point cloud of each object is encoded in a set of latent vectors. 
4. Line 130 states that the set point cloud policy takes in the observed point cloud $P_{obs}$. Doesn’t the policy network take in a set of object point clouds {$P_1,...,P_C$} instead? I recommend the authors more clearly differentiate point cloud sets and the point cloud of the whole scene in the method section.
5. Line 175, why is the MLP trained to predict Chamfer distance between latent vectors?
6. Why did PASTA achieve better performance on CutRearrangeSpread(CRS) than CutRearrange? Does this have to do with the different training for the two tasks?
7. Looking at Step 3 of CRS-Twice in Figure 3, the predicted subgoal does not match the actual point cloud of the left object but this task is still completed successfully. Can the authors provide more insight into the robustness of the algorithm to errors in subgoal prediction and whether the policy networks are using the predicted subgoals?
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: 
This paper presents a planning framework for long-horizon manipulation over deformable objects. The approach authors present demonstrate good generalization ability to unseen number of objects and can incorporate quite a number of skills that involve deformable objects manipulation. The paper is important to the community in terms of scaling deformable object manipulation to long-horizon execution instead of always focusing on single-stroke, single-object manipulation. 
strengths_and_weaknesses: 
Strengths:
- The paper presents an approach that shows quite an impressive plan over deformable object manipulation.  The experimental results and the videos are quite impressive. I think the authors are tackling important questions in terms of deformable object manipulation.
- The approach is designed in a way that it can learn skills in a simple simulator setting and use the skills in the unseen configurations which contain more objects.

Weakness:
I have quite a lot of comments on the paper writing. I think the paper writing needs improvements in terms of precise wording and related work citations:
- Line 4: I suggest authors can write more explicitly why "full-state" or "short-horizon" assumptions prevent the use of deformable objects
- Line 8: are authors using any other 3D observations? If not, "such as" is not precise here. 
- Line 22: Are the skills (temporal abstractions) for deciding which tools to use? or the abstractions of execution with skills? Make discrete decisions of which tool to use also sounds like a single-step decision.
- Line 27: One issue I have is, why "atoms"? I think the community typically compares state abstractions with physical states or raw sensory representations. I don't think anyone does "infinitesimally" timesteps. The logic sounds ok but quite trivial. I think the authors can do much better in describing this part.
- Line 28-35: I found this paragraph overclaiming. A lot of works use temporal abstractions in robot research and spatial abstractions. I think the authors are considering the context of deformable objects. Please change "complex objects" to "deformable objects" to make this paragraph sound specific and precise.
- Line 40: What is "nearly infinite" degrees of freedom? Authors should provide a citation that strictly defines such an ambiguous term. Otherwise, I think “infinite” is sufficient
- Line 58: Task and Motion Planning doesn't construct the models --- they assume/use the models given. 
- Line 72-78: missing citation -- such as regression planning network[1], which is grounds in visual observation but generalizes to long-horizon tasks.
- Line 120: I am not very convinced by the term. It seems to describe a single abstraction that is both spatial and temporal. But from my understanding of the paper, spatial abstraction is on the 3D representation, and temporal abstraction is about the tool-use skills. I don't think this is an appropriate name for abstractions in the paper.
- Line 168: Reward is quite an RL-oriented terminology, while the work focuses more on the planning work. I suggest using the term "cost" to better align with the planning literature. Also, that would align better with how pseudo code presents the approach.
- And I think the specification of goals is not ideal. The goal is given in terms of point clouds, which means that the execution of the task is required before planning a sequence for such an execution. While the goal specification is not the main point of this paper, it hinders the proposed approach's significance. Specifying goals simply could make the paper to be more significant. I think the authors should also discuss this limitation in Section 5. In my opinion, the unsupervised learning part is quite minor compared to the limitation of goal specification.


[1] Xu et al. Regression Planning Networks


robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are not well addressed
summary_of_recommendation: Overall, I would like to give weak accept to this submission based on its impressive results and the hard problem the authors try to solve. However, I think the paper presentation needs a boost in quality in terms of precise wording.
issues: I hope most of my comments in the "weakness" part can be addressed.
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: This paper presents a learning pipeline that combines spatial and temporal representation to conduct long-horizon planning for deformable object manipulation.
strengths_and_weaknesses: Strength:
The paper is very well written and easy to follow. The framework is extensively tested with simulation and real robot experiments. The website and appendix provide complementary information which helps the reviewer to better understand the proposed approach. 

Weaknesses:

The reviewer found the Limitations paragraph unsatisfactory. The first limitation talks about the limited generalization capability of learned parameters to different tasks which are acceptable. The second limitation on the sim-to-real gaps seems to be more like a statement rather than pointing out the limitation.

Minor:
- P.4. Line 99, one of the K skills, K is not defined.
- P.4. Line 122-123 “a single-stage task with a single tool and can be just for a single object”, maybe it is better just “a single-stage task with a single tool for a single object” 
- P.5. Alg 1  Decode subgoal from $U^h$ should be $U_h$?
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: A decent paper with extensive validation on simulation and real robot experiments.
issues: - Limitations could be elaborate on a bit more. 
- The paper considers 3 skills, respectively push, cut, and spread, and the longest task requires applying 6 skills in sequence. This makes the possible combination of actions #3 * 2^5# = 96 variations. This is a rather small search space to find the correct sequence, the reviewer wonders if the authors could comment on how well the method scale to larger search space for task sequencing.
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: This work is about long-horizon elastoplastic object manipulation, including multiple tools. This paper emphasizes that their method incorporates spatial abstraction and temporal abstraction. Here, spatial abstraction is about decomposing a dense scene into a set of objects and representing them with embeddings, and temporal abstraction is about high-level reasoning over different skills. PASTA executes a trajectory by solving a two-step optimization problem where the first step finds an optimal sequence of skills and the second step finds an optimal sub-goal for each skill.

strengths_and_weaknesses: **Advantages**
- Suggest planning with tool changes and learning individual policies for each tool.
- Simulation and Real-world experiments using different types of elastoplastic material(sand and real dough).

**Weaknesses**
- One of the main limitations of this paper is that their ‘point cloud policy’ or the action generator is not evaluated in or transferred to real-world experiments. For the real experiment, they utilized heuristic controllers for the skills.
- PASTA does not allow feedback between each subgoal in a sequence (k_1, …, k_H). Planning in the long-horizon is a great direction; however, not updating subgoals during the long-horizon manipulation makes the method susceptible to disturbances.
Requires to specify the number of steps during tests. Performance efficiency and accuracy depending on user input can also be the limitation of this work. 
- PASTA relies on manual pre-designed skill definitions (M_k_h, N_k_h). 
- Lack of clarifications/typos in some texts 
    - Line 86-88: I am not sure how ‘using 3D set representation extracted from point clouds’ can directly be linked to generalizing compositionally to tasks with "more objects and longer horizon”.
    - Line 130 : can you clarify the length of action from \pi? The output actions per each skill should be greater than 1, especially for spreading, which makes the robot move the roller back and forth. 
    - Line 138 : definition of N_o and N_g
    -Typo in line 116: “We can the encode~ “
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: Elasto-plastic objects are generally hard to manipulate with robotic fingers or grippers, thus requiring tool use. This study is powerful in that it allows deformable object manipulation with multi-tools. However, I am hesitant to recommend this paper unless the authors address the major limitations. As discussed in 'issues', the major concerns are 1) partial sim2real transfer and potentially misleading evaluations and 2) lack of justifications in model choice/design (2-4 bullet points in issues). Other than that, thorough real-world analysis and experiment are great, and the paper overall was enjoyable to read.

issues: - Doughs in real life are much more challenging to handle because it sticks to the desk and the tool. Also, dough's material property is time-variant. (Get firmer and less sticky as it dries). This seems to be a great huddle for PASTA's sim2real transfer. Including the authors' insight on integrating these uncertainties with an action generator will make this study much stronger. 
- Justification of using tool point cloud: Why use the tool 'pointcloud' for Point Cloud Policy instead of the estimated pose and the type of the tool? For example, the tool can be illustrated with 6 numbers (x,y,z,roll,pitch,yaw) where the type of the tool can be expressed in one-hot [0 ... 1 0 ..]. I wonder how robust pointcloud-based tool input regarding the tool occlusion caused by the robot arm/gripper and dough. 
- Why do you think decoupling subgoal and skill sequence for optimization is a great choice? Why not optimize them simultaneously?
Why is it better to use a reward 'predictor'? (Line 173) Can we convert the decode the latent codes and directly get the reward? For example, you can explicitly use grid search to get the similarity score.
- I feel that the author's heuristic controllers in the real world might conceal the sub-goal and action error. In detail, in 'Fig.3. CutRearrange' step 2 (top sand) shows that PASTA shows some error at 1) setting accurate subgoal and 2) moving the dough to the target place. Ironically, the errors devoted to increasing the 'Final' accuracies, which I think is odd. Can the authors explain this?

**Writing**
Explicitly making a section describing the training process and the entire loss formulation will help readers to follow the paper.
How much train and test data? What is their distribution?
review_title: null

Meta-review: metareview: This paper presents a planning approach that learns spatial/temporal abstractions of a set of skills to cut, spread and push deformable objects from demonstration.

The quality and originality of the paper are very good. Also, the significance of the work is high with the potential to have a major impact on the robotics learning community.

Overall the approach is sound and the results are impressive, most of the clarifications/minor issues from the reviewers were addressed successfully.

The supplementary material is to be commended.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)