Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper aims to improve the efficiency of motion planning problems in dynamic environments, such as with moving obstacles or multiple manipulators. The approach taken is graph neural networks and temporal encoding framework. Trained with imitation learning and data aggregation procedures, the proposed methods significantly reduce collision checks and planning time, at the cost of reducing planning success rate.

Concretely, the core building blocks are graph neural networks, attention mechanism, temporal encoding of moving obstacles, and imitation learning with DAGGER style of data aggregation. Extensive experiments are provided to compare the proposed method with search-based baselines, ablation baselines, and end-to-end RL methods.
strengths_and_weaknesses: Originality

This paper takes a graph neural network (GNN) approach and temporal encoding to tackle motion planning in dynamic environments. It is a novel way of applying existing approaches such as GNN and attention mechanisms to an existing problem. Many details were reasonably designed, e.g., the temporal encoding of moving obstacles.

Quality and Clarity

The algorithm is reasonably designed, justified by the provided ablation studies of module and data flow choices. The approach is clearly presented, backed with diagrams and example drawings.

There are quite a few typos throughout the paper. Please do more proofread for future revisions. Just to name a few, "(" in L112, why (k) is needed for the aggregation function in eq(1)? L118 "has to". 165 "E_j" -> "E_i".

Significance

The problems of motion planning in dynamic environments is an important problem, relevant for a wide group in the community. Leveraging GNNs (likely the first time) to solve this problem is interesting. If the pros and cons are thoroughly analyzed and presented, it can be of greater significance.

This is an important issue to address. It is not a surprise using a GNN and temporal embedding, trained on the same set of static and dynamic objects, can achieve with more efficient search heuristics. However, this efficiency benefits comes at a price, sacrificing the success rate, i.e., paths cannot be found in many cases. The authors did an attempt using backtracing, but only showed the result on the most simple setting, two-DOF arms. Yet it is not strong result (98% for random and 89% for hard). From a reader's point of view, understanding this trade-off in a more systematic way would be a key contribution. See also Questions.
questions: 1. How is the speed (saving number of collision checks) in trade-off with quality (success rate of finding paths), and how does this trade-off vary with different amount of training? E.g., currently 2000 problems are used for training. Is the performance increasing with more training problems? Is it in a linear or log trend? Does it saturate?

2. The separation between random and hard cases is determined by Dijkstra-H. Is there a justification of this choice or because there are no better ways? It's also helpful to provide a direct measure on the complexity of the planning problems.

3. Using the attention mechanism to encode obstacles is not analyzed separately, i.e., why is attention mechanism the most proper to model the obstacles?
limitations: No specific analysis or discussion is provided on the limitations by the authors. Though, from the result section, it can be observed the proposed GNN based motion planning has a reduced success rate compared to the baseline SIPP. More comprehensive analysis on this would be greatly appreciated by the community. Refer to Questions.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes to use a learning-based approach for motion planning with dynamic obstacles. Each possible robot configuration (after discretization) is represented as a node. This method assumes it knows the full trajectory of the obstacles. It first generate expert trajectories using some sampling-based path planing method (SIPP) and use that as the ground-truth and try to imitation that policy. The performance is further enhanced by using DAGGER. The advantage of using learning-based approach to imitation the tradition approach is that the learning-based approach is much faster.
strengths_and_weaknesses: [originality] This work is inspired by the transformer literature and successfully modify it to the motion planning community. I believe this paper has original contribution for this adapatation.

[quality] I believe overall this paper gives detailed and comprehensive evaluation of the proposed method. However, I found the comparison between the proposed method and other learning-based method is missing. For example, one simple baseline I could come up is using [1]’s network, with an additional input of all the possible obstacle trajectory (since the full trajectory is known), and output the next optimal configuration.

[clairty] I think the technical part is clear except for one point: this paper claims previous method considers fixed graphs ([39, 21]). Then I’m wondering how the obstacle trajectory is represented? My understanding is the obstacle’s full trajectory is represented as a node in the graph. However, in that case the graph is fixed and some previous methods need to be considered in the experiments (such as [21, 39]). The authors may want to clarify this.

[significance] The proposed method is much master than the traditional optimization-based method. This can be a great advantage for many applications. However, the performance drop seems to be huge in the hard case (Figure 4 and Table B2). Since motion planning would be safety-critical in many cases. This may be a major limitation for the proposed method. I think it would be better to provide some illustrations and convincing improvement direction in the paper. Another possiblity to make the contribution more significant is to consider cases where the object trajectories are not known thus prediction and rapid re-planning is needed.
questions: The following questions are essentially what I said in the above section:

1. How is the obstacle trajectory represented?

2. How to deal with cases where the obstacles' trajectories are unknown?

3. Is there any possible comparison with other learning-based methods?
limitations: N/A
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes an imitation learning approach which mimics the behavior of a search-based configuration space motion planner with dynamic obstacles (SIPP [32]) using DAgger. Planning is performed on a sampled road map graph which is encoded with the motion of a dynamic obstacle and the state of static obstacles in a GNN. The method seems to assume a fixed shape and kinematic structure of dynamic (robot arms) and static (boxes) obstacles. The method is evaluated in simulation and compared in planning performance metrics with ablations, SIPP and a simpler Dijkstra-based baseline. The approach well improves the required computation in terms of collision checks, while keeping a high level of success rate and path efficiency.
strengths_and_weaknesses: Strengths:
* The approach seems novel and effective in cloning the planner behavior for the trained environment settings.
* The paper is well structured and can be easily followed. 
* Improvements over ablations, the search-based planning baseline SIPP, and a simpler Dijkstra based planner are demonstrated in several planning problems with various robot kinematics and difficulties.

Weaknesses:
* The writing requires significant proof reading to correct spelling and grammar mistakes.
* The title mentions "Manipulation Planning" but actually there is no object manipulation performed. The approach performs motion planning. Title should be changed accordingly.
* The supplementary material should provide the network details to allow for reproducibility of the work. Ideally, also code and datasets should be provided publicly.
* l.115 the explanation of where the attention mechanism will be used is confusing, because the concepts are unclear at that point of reading. Just introduce the attention mechansim abstractly.
* l. 143, please introduce the subscript notation v_i,v_j, x_i, x_j and that the subscript links nodes and respective components of the features. the y needs to be separated with a comma.
* The notation O = O + TE is unclear. first of all, this is not a proper equation but an assignment. Rather use a new symbol for the left O. Also, O and TE as well as the + operation are not defined.

questions: * l. 226 it's unclear how the Dijksta-H baseline works from the description. How does it handle arrival time and does it use collision checking ? Please revise and provide more implementation details in the supplementary material.
* l. 246 is SIPP guaranteed to find optimal paths and for which planning problem ?
* The ablation study in 5.2 only reveals little improvements by the individual components relative to the performance of the baselines. What is the overall performance gain by all the components over the basic GNN ?



limitations: * The paper does not address limitations and assumptions properly. For instance, it should be clearly stated that the model needs to be trained for the specific actor arm, the obstacle arms and the static obstacles which it is tested on, i.e. that specific kinematic structures and obstacle shapes are assumed. How can the approach be generalized to arbitrary obstacle shapes and arm kinematics?
review_title: null

Meta-review: metareview: Robot motion planing in dynamic environments remains a significant problem. All reviewers consistently agree that the suggest GNN approach in this paper has useful merits, is of general interest, and that the paper is above the publication threshold. Detailed comments of the reviewers provide a good source for some fine-tuning improvements of the paper.
confidence: Certain
Decision: Accept