Venue: CoRL 2022 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 proposes an algorithm for object grasping in clutter from a truncated signed distance function (TSDF) volume scene representation. The algorithm represents a grasp using a pair of points where the two fingers of a parallel jaw gripper are supposed to contact the object i.e. antipodal contact points. One of the pair of antipodal points is obtained by sampling from the input TSDF, and the other one by extending a vector backwards along the surface normal and intersecting it with the TSDF zero surface. Training is performed by generating data from a set of mesh objects arranged into cluttered scenes, labelled with theoretical antipodal score.

Experiments performed in simulation and on a real robot measure grasp success (GSR) and the success rate of clearing a clutter of objects (3 tries allowed per object to pick it up) (GCR).
strengths_and_weaknesses: ##  Strengths
- The paper is well written and understandable, figures are useful
- While this paper is not the first to introduce the idea of operating on a TSDF instead of a single image, the introduction section motivates the use of TSDF well
- Real robot experiments are conducted to verify the simulation results

## Weaknesses
- It is not clear how different this paper is from others that also operate on a (T)SDF volume, especially [12]
- One key difference is that [12] predicts 6-DoF gripper poses instead of contact point pairs as proposed here. However, the current experiments showing incremental performance improvement over [12] do not control for other factors, hence it cannot be said that the performance improvement is due to the grasp representation proposed here. For example, [12] uses an 8-layer network, while the network in this paper is more than twice deep (see appendix). EDIT: This was addressed in the authors' response below.

### Smaller points
- L15 "dexterous": this paper does not address dextrous manipulation
- L35-36: meaning unclear, difficult sentence to understand
- Section 3.5 is mostly redundant, the space can be used for other purposes e.g. images of real robot execution 
- L189: "closed loop grasping pipeline": the pipeline described here is not closed loop - that would require the grasp sucess/failure signals influencing future grasps.
- L240: will the randomly paired points be antipodal? if not, why do this, because real world points will all be antipodal
- Quantitative comparisons to baselines in Table 3 are technically not comparable because they do not use the same test sets / scenes. This needs to be discussed in the paper if the current presentation is maintained.
- Supplementary material not referenced
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I am voting to reject this paper because it is unclear whether the incremental performance improvement over [12] is because of the proposed grasp representation or a deeper network.
EDIT: Raising my rating to Weak Accept because the authors' response addresses my main concern.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     
issues: Ablation study experiments, especially to tease apart the effect of the proposed grasp representation.
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 proposes a grasp generation pipeline that reasons over a TSDF volume. To achieve this, the pipeline learns a grasp analysis model that reasons directly over meshes. This approach is validated over a wide range of experiments grasping objects in cluttered scenes. This pipeline significantly outperforms common baselines from normal-based grasp selection in both real-world and simulated experiments.
strengths_and_weaknesses: Strengths:

1. The paper is well written and overall easy to follow.
2. The new volumetric approach to grasping leads to major improvements in performance in comparison to normal-based approaches
3. This pipeline is validated over a large number of experiments, both real-world and simulated.
4. The paper is very clear and straightforward on its limitation and areas for improvement

Weaknesses:

1. The authors could improve the visual depiction of the pipeline for readers unfamiliar with the state-of-the-art grasping pipelines.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This paper presents a novel contribution to the field of grasping. The approach builds on top of previous work on volumetric grasping and demonstrates how this pipeline outperforms previous work on a wide array of experiments. The paper is well written, is technically correct, and a relevant contribution to the manipulation community. The reviewer recommends its acceptance.
issues: My only issues with this paper lie in the presentation:

1. Figure 2. could be improved to better visually depict the proposed pipeline for unfamiliar readers.
2. Figure 4. can be confusing at first and is not well labeled.
3. The authors remark on the fact that their approach is trained exclusively on synthetic data, which is very impressive. However, this seems to be common among other pipelines as well. The reviewers should be careful with this statement.
4. The paper does not show any images of the real results or experimental setup. While not critical, it would be helpful to understand the difference between the synthetic training setup and the experimental setup.
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 proposes a grasping-in-clutter framework for computing grasping contact pairs from a Truncated Signed Distance Function (TSDF). The paper argues that TSDFs capture more of the scene's geometry and the contact pair formulation removes the normal-based assumption present in other frameworks. From the TSDF, the Marching Cubes algorithm generates a mesh whose vertices serve as a grasp candidates. The framework next uses an antipodal grasping rule to find valid contact pairs, which are fed into a Contact-Point Network (CPN) that evaluates them for grasp quality. The CPN also takes the TSDF as input, allowing it to account for scene information. Finally, the gripper is collision checked along various approach vectors to find a valid one. To train the CPN, a large set of contact points are scored analytically and in simulation, based on whether they are graspability and collision-free. The method is compared against three other normal-based approaches, two of which use TSDFs.  
strengths_and_weaknesses: This paper focuses on a very relevant application area (grasping in clutter) and by using TSDFs the framework is able to factor in collision information about the scene. Removing the assumption to grasp normal to the object's surface could enable a wide range of grasping. The paper is well-organized and the related work highlights the motivation for the methodology. The experimental section compares to three state-of-the-art methods across a large set of simulation experiments and a clutter removal experiment. The discussion on how TSDF-based methods can struggle when grasping thin objects was particularly interesting.

The paper proposes a closed-loop grasping pipeline stating that as the robot moves the input from the sensor can be used to improve "the performance of the contact point detection". However it is not clear how the performance is improved over time. How is new information incorporated in? Would the grasping point ever change? Is the TSDF updated? Currently it is not clear how the closed-loop pipeline is implemented.

Particularly because two of the compared methods (VGN and VPN) use TSDF, it would be great to get further insight on how the proposed method uses the TSDF in a way that leads to improved performance. This could help tease out where the improvement comes from and how the aspects of the proposed approach connect back to the performance. 
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I am weakly recommending the acceptance of this paper. The paper builds on existing work to bring together several interesting ideas in order to enable a more generic form of grasping. The experimental section is fairly extensive and compares against several other relevant algorithms. The closed-loop pipeline is currently not clearly explained and there are a few clarifications (described below) that should be made.  
issues: Connecting to what was stated above, it would strengthen the paper to clarify how the closed-loop pipeline is implemented. This seems particularly important given the emphasis of the closed-loop nature in the introduction. 

Related to this, Sec 3.5 makes the comment that the modules "can" be implemented with GPU acceleration such that the framework can be efficiently executed closed-loop. If the experiments were done without this acceleration, can the process still be executed closed-loop? What aspects of the pipeline are a bottleneck? 

Within the introduction, I didn't quite understand what meant by "first assumption may introduce position shift since most of the time the surface position is not exactly the same as the end-point position of the gripper,". Is this that the surface position is not the desired position of the gripper during the grasp? What is meant by position shift, is this during the grasping process? Perhaps clarifying the wording, or further using Fig. 1 as an example may help clarify this motivational assumption. 

A few smaller comments, questions, notes: 
- Making the annotations on Fig. 1 larger would make them easier to see. 
- The appendix discusses that the step size of the Marching Cubes algorithm was set such that the number of vertices is around 1000-8000, which is "sufficient for covering the entire scene". How was this number chosen and how would changing this parameter impact the performance of the algorithm? 
- Sec 5.1 mentions that any object that could not be grasped three times was manually removed. Where the type of objects that needed to be removed flat-shaped objects, as discussed in the limitations, or were there other classes of objects that the grasping systems struggled with? 
- Given that the best performance is bolded in Table 1, it would maintain consistency to do a similar bolding in Table 2.
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 presents a 7-dof grasp point detection method. The proposed method considers the TSDF volume and trains a network to detect the grasp point using simulators. The paper validates the method by comparing the method with other methods using various objects in simulations and real robot experiments.
strengths_and_weaknesses: Strengths
- The introduction describes clearly the background and contribution of the paper.
- The related work section introduces related topics and papers and compares them with the proposed method.
- The method section, data section, and experiment sections clearly demonstrate the method, data, and experiments, respectively.
- The limitation part describes the limitation of the method precisely.

Weakness
- All issues have been resolved in the revision.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This reviewer recommends Weak Accept. The paper is written clearly and experimental results support the claim of the paper well. However, the reviewer is not familiar with the field of the paper and is not sure how novel the proposed method is and how difficult the problem of the paper is.
issues: All issues have been resolved in the revision.
review_title: null

Meta-review: metareview: This paper proposes a 7DoF grasping method which is based on TSDF for scene representation to compute grasp contact pairs, where the Marching Cubes algorithm generates a mesh whose vertices serve as a grasp candidates. These contact pairs, TSDF, vertex normals and surface vertices are used as input for a proposed a Contact-Point Network (CPN) to compute grasp quality. The paper is well written and easy to follow. The use of TSDF for an application of grasping in clutter to extract collision information about the scene is sensible. Experiment results show improvements. There are real robot experiments to verify the simulation results. 
The authors have clarified most concerns during the rebuttal phase. Though there are other issues, e.g. it would also be helpful to know if changing the grasp pose at each close-loop execution step ever causes discontinuous or undesirable behavior, I would like to recommend the paper can be accepted.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)