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: 1. This paper proposes a data-driven method for time-optimal trajectory generation for quadrotors.
2. Supervised pretraining and bayesian optizatimization & reinforcement learning are combined, which is interesting. The three stage training scheme is one of the core contribution.
3. Superior perfromance is validated both by simulation and onboard tests. 
strengths_and_weaknesses: ### Strengths
1. Three-stage training scheme is interesting.
2. Modeling min-time trajectory generation problem as a seq2seq learning problem should be a good contribution to the community.

### Weaknesses
1. **The literature review and experimental comparisons are not sufficient**. The authors may consider comparing your methods with the following: 1) "Optimal Time Allocation for Quadrotor Trajectory Generation" by Fei Gao et al; 2) Time-Optimal Planning for Quadrotor Waypoint Flight by David Scaramuzza et al; They are both open-sourced and highly related to your topic. Minimum snap (MS) is now a relatively weak baseline and 20% faster than MS may not be a very supersing achievement.

2. **Why using BO for the labeling of the sceond stage?**. BO may be too slow for this task and may limit the scale of the dataset for the second stage. The authors may consider using stronger methods as listed in 1. It can also be expected that the performance of the proposed pipeline is affected by the scale of the dataset for the second stage. A stronger method for the second stage may lead to a much stronger performance.

3. **Still some gap to online motion planning** The authors do not incorporate the proposed method in a planning system in the wild. It will be much challenging for the proposed method to work generally in the wide. It is still unknown whether it can generalize that well. Although this point is also listed in the Limitations section, the reviewer thinks this is still a marjor weak point.

4. **The scalability problem**. The authors mention that, even for this model in limited ODD, it still takes weeks to train. Many intermediate methods are time consuming. The problem comes that whether the pipeline is still feasible for a motion planning method in the wild (the scale of the dataset should be much much larger than the current one). The authors may provide further thoughts on this issue.
  
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: The idea is interesting, but still some gap to a solid contribution.
issues: The four weak points in the previous section may all be justified in the response.
review_title: null

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: The work presents an approach for real time inference of near-optimal time allocation for trajectory segments of minimum snap trajectories for quadrotors. To this end, the work utilizes a seq2seq model that is trained in three stages i) imitation learning from suboptimal trajectories ii) Bayesian Optimization using a smaller portion of optimal trajectories iii) RL refinement. To demonstrate the quality of the proposed method, the trajectories generated with this method are tested in simulation and on a real quadrotor platform.
strengths_and_weaknesses: Praise:
* The work follows a generally interesting avenue of real time trajectory generation for very computation constrained platforms, such as drones.

* The work demonstrates results using a real quadrotor platform.


Concerns:
   * To be honest I am not sure what specifically is called a “trajectory” in the context of this work, i.e. what parameters of the trajectory are minimized, what is trajectory representation? It does seem that the output of the seq2seq model is simply time allocations and smoothness weights, but how is trajectory itself generated, i.e. polynomial parameters or maybe quadrotor thrusts/velocity commands? 

   * What are the assumptions in this work? From what I understand the work assumes not only knowledge of the full dynamics model of the quadrotor, but also presence of a tracking controller that can go between provided trajectory key points, given the budget of time. The combination of both seems to constitute the feasibility set P_T.
 
   * The method is rather complex using a 3 stage training procedure and 3 different approaches, which is definitely error-prone and, likely, introduces lots of tuning and hyperparameters. Modern RL methods under similar assumptions can solve for direct quadrotor commands (with implicit planning) and do it in hours (worst case days), not 4 weeks as stated in the Limitation section while showing very compelling results for various tasks in quadrotor flight (see [5] below just as an example).

   * On the other hand,  work [1] presents seemingly strong baseline for simultaneous planning and tracking (there is generally a larger body of work from the same lab for time-optimal, high-speed robust quadrotor flight). It surely uses simplified assumptions on the planning part, yet demonstrates high performance even beating human pilots. The work under consideration does not seem to have an advantage here since it also lacks specific guarantees (it uses NN trained through BC and RL) and, I believe, relies on the accuracy of the model. Hence, it would be interesting to have some comparison or strong justification why such comparison is not appropriate.

   * It is not completely clear why the authors need smoothness weights and time allocation at the same time. Both represent just normalized (over the entire trajectory) scaling of the cost per segment, i.e. two scaling parameters seem redundant.

   * Literature overview is not complete, please, include (at least) these works with explanation of differences/advantages of your work:
      * [1] Angel et al. Model Predictive Contouring Control for Time-Optimal Quadrotor Flight. 
      * [2] Panicka et al. Learning Minimum-Time Flight in Cluttered Environments.
      * [3] Romero et al. Time-Optimal Online Replanning for Agile Quadrotor Flight.
      * [4] Loquercio et al. Learning High-Speed Flight in the Wild.
      * [5] Molchanov et al. Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: As stated in previous sections, I have problems with understanding the significance of the potential impact this work can make, especially considering missed references in the related work section.
I am, clearly, not an expert in the area of planning for minimum snap quadrotor trajectories, hence, maybe, after some strong arguments I might change my opinion. But, for now, I am inclined toward rejection of the paper. 
issues: See Strengths & Weaknesses
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 trajectory optimization problem for a quadrotor to fly through multiple way-points in as short a time as possible has very complex dynamics. The problem is how to deal with the varying number of way-points and large computational cost, especially when the trajectory must be generated in real-time. Usually, simplified dynamics models or polynomial approximations of limited intervals are used, but these methods have limited performance. In this paper, the authors have succeeded in constructing a seq2seq model that can generate trajectories with a lower computational cost by approximating the exact solution with a DNN model and then generalizing the trained DNN model by reinforcement learning.
strengths_and_weaknesses: - The paper is consistent and clearly presented from the problem statement to the proposal of a solution and its evaluations through experiments on real systems.
- The most significant contribution of the paper is the proposal of a framework for solving the problem stated in the abstract, but the reader does not have sufficient information necessary to implement the proposed framework. For example, the network structure of the seq2seq model and the method of preparing training data.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The consistency of the paper, the clarity of the presentation, and the implementation in real environments are highly evaluated, but on the other hand, the novelty of the learning algorithm is not high, and the evaluation on generalization performance is not sufficient. Therefore, it was evaluated one rank lower than the highest.
issues: - In order to discuss the scalability of the proposed method, specifics on the network size of the seq2seq model and the details of the GPGPU environment used in the experiments are required.
- In addition to real-time performance, it would be better to discuss battery consumption.
review_title: null

Meta-review: metareview: The paper presents a data-driven method for solving the planning/control problem of quadrotors with multiple way-points.

Strengths:
- Modeling min-time trajectory generation problem as a seq2seq learning problem should be a good contribution to the community (SHLP)
- Evaluation on real system (a94g)

Weaknesses:
- insufficient literature review (SHLP)
- scalability (SHLP,a94g)
- wrong statements about comparison to prior art (7heK)
- missing baseline/comparison to e.g. Angel et al (7heK)
- an improvement over a single baseline that is not state of the art anymore (SHLP)

This is just an excerpt from the reviews, and I invite the authors to address all points raised by the reviewers.  

Post-rebuttal:
Two reviewers suggest weak accept and one reviewer suggests weak rejection.
The authors have addressed some concerns in the response.
I ask the authors to make sure the manuscript is adapted to clarify the concerns itself.
I would also ask the authors to add ablation studies showing the contribution of the components. What if parts of the 3 learning stages are omitted, for instance? This is a borderline paper, but given the reviewer scores, I am leaning towards acceptance.  
I leave the final decision to the PC.
confidence: 3: The area chair is somewhat confident
Decision: Accept (Poster)