Venue: CoRL 2023 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_of_paper: This paper proposes an architecture for estimating the distance between links in a robot model. The key novelty of this paper is that they split up the global robot model into links and link poses and estimate the pairwise distance between links.
review: This paper presents a novel approach and demonstrates improvement over a comprehensive set of baselines. 

Given that the intended use case for this is motion planning, it would be good to see some speed comparisons between a standard collision checking approach and this network.

Additionally, this approach doesn't extend to the types of problems that collision detection is most often needed for (detecting collisions between the robot and an arbitrary environment geometries).
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This paper presents a novel approach for collision distance estimation and demonstrates improvement over a comprehensive set of baselines. The paper would benefit from some experiments regarding space and memory tradeoffs between learned and classic collision detection in motion planners.
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 presents a learning based method for collision distance prediction and its' application in collision detection for motion planning of manipulators. The algorithm uses a pairwise collision distance computation for multi-part objects to address the computational complexity of high-dimensional systems. The learning architecture uses an encoder to compute the shape feature vector from the point cloud of each shapes and then a regressor that uses the shape feature vector along with the transformation between the pairs of geometric shape to predict the minimum distance between them. Experiments evaluate the performance of 7-dof panda robot with varying the number of robots and distance between them in each environment. The performance show the proposed method excels in accuracy, safe-FPR, MSE and AUROC when compared to bounding volume method, Joint and position nn, joint nerf, clearance net and Diffco. 

review: The major strengths of these work are:
- Computation of collision distance as a pairwise problem for multi-body system is intuitive. This is how even deterministic method of computation of collision distance is performed. 
- Separation of encoder and the regressor in the system architecture is useful in reducing training effort especially in multi-robot scenarios. 
- The authors provide comparative evaluation with other learning based methods such as ClearanceNet. 
- The authors also provide a variety of evaluation metrics and demonstrate the performance for high-dimensional robotic system (28-dof).
- The authors provide a real-world demonstration of their framework.  

The areas of improvements are:
- Clearly state the changes in the system the model can handle without the need for retraining.
- Empirical comparison with GraphDistNet
- Variety in the obstacles and multi-robot systems by changing distance of the bases and workspace obstacles of different shapes other than rectangular and their impact on the training effort.
- Inclusion of other learning based collision detection work in related work. See the list in questions for rebuttal.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, the paper presents an interesting idea of computing collision distance between objects by decomposing it into pairwise distance computation. This is evaluated for high-dimensional systems and compared with other known collision distance method computations.
review_title: null

final_score: rating: Strong Reject: I recommend rejecting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary_of_paper: This paper aims to develop a data-driven collision-distance estimator that finds the distance between multiple manipulators. However, this is challenging due to the complexity of high-degree-of-freedom robots and the environmental variabilities. To figure it out, this paper proposes PairwiseNet, a collision distance estimation method that finds the pair-wise distance between two sub and known geometries of robots. The main contributions of this paper is as follow:
-	Efficient distance estimation formulating easily solvable smaller sub-problems  
-	No need for additional training or modification

review: Strengths – The main strength of this paper is the decomposition of the distance estimation with complex geometry into parallel sub-problems. 

Weaknesses – The method requires known geometries that cannot be scalable and generalizable to new setups. Further, there is no comparison with conventional/representative distance estimation methods such as the GJK method.

This paper needs to clarify and improve a few things:
- A major concern is the scope of this work and its limitation. This work assumes the known geometry of robots. The pairwise distance estimation seems to be a good idea, but current distance computation already performs in the same manner. Further, the existing algorithms such as GJK algorithm, which has been used in Flexible Collision Library, are quite efficient if simple geometries are given. Unlike the proposed method, the other methods are scalable/generalizable to variants of geometries that can be given by sensors. Thus, although this proposed method shows efficiency in the defined setup, it is hard to know if we really need this without further evaluation with other distance computation methods.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: The proposed methodology shows good accuracy in the setup. However, the method requires strong assumptions and missing comparisons with major methods in the field. I would like to recommend improving this paper.
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 proposes a method that views collision detection of articulated bodies as the computation of the distance between the robot's links pairs with a transformation. Once trained, this method is able to extend to high DOF and multi-robot scenes. Experiments also show the efficiency and effectiveness of the method.
review: Strengths:

- Using a neural network to encode two known shapes with any transformation for distance is interesting. It is efficient and can be extended to high DOF robots. 
- The experiments show the efficiency and effectiveness of the method.

Weaknesses:

- The training complexity of this method may increase quadratically as DOF increases, e.g. every link pair of a humanoid robot will be considered
- Each robot link has relatively simple geometry and may be represented by several convex shapes. Real-world experiments also decomposite obstacles to several convex shapes.

I think this paper's idea is interesting, decompose collision checking into subproblems and consider using a neural network to encode the relative transformation of shapes and get their distance. However, by this kind of decomposition, the shapes are nearly convex. I am curious what is the performance of directly computing the cage (maybe a combination of several convex shapes) of the shape and using GJK to compute the shapes' distance after transformation.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: I think this paper's idea is interesting. However, after the problem decomposition, I think the subproblem may be easily solved by traditional methods.
review_title: null

Meta-review: The reviewers find this work to be both interesting and novel. The comparison to GJK nicely shows the improvement of using the proposed approach over common analytic approaches to collision checking.

I generally agree with the reviewers that comparing to GraphDistNet would improve the paper and I thank the authors for their attempts to do so and understand the difficulties in making this happen. If this could be included for the final version of the paper it would definitely increase its value and quality.
Decision: Accept (Poster)