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 presents an online learning approach to robust obstacle avoidance in the presence of unmodelled system dynamics and environmental perturbations. The proposed algorithm performs online trust-region optimization of a linear dynamic controller using an obstacle margin loss regularized by perturbations to the nominal plan, to ensure robustness while straying as little as possible from the nominal plan.
review: Review Summary
Overall the problem is well-motivated, and the problem well set up. There are a couple of unclear sections in the technical presentation (see below), but overall the approach seems both correct and reasonable. The empirical results are good.

Detailed Comments
* The symbol $A$ seems to be overloaded: in eq. 2, it is the controller, and in eq.1, it is the state transition matrix. Is this correct? 
* In Section 4.3, it is unclear what the connection between the optimization in eq. 9 and FPL is. It seems to just be a trust-region (potentially nonlinear, non-convex) optimization problem. What algorithm is used? If a local optimization solver is used, how is the solution initialized? 
* It seems that the horizon $H$ should be carefully chosen to be long enough to allow for observability of unmodelled dynamics at all times, while minimizing it for computational tractability. Is this true? How is it chosen?
* The switching of planning algorithms between sim and real is odd - why not try both RRT* and A* in both sim and real, as baselines?
* The robustness of both A* and RRT* is dependent on the margin, as identified in the paper. However, this can affect the planner's ability to find narrow paths - it would be nice to highlight this empirically and compare it to the proposed approach.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall this seems like a strong paper, though there is room for improvement in the technical presentation, and in the explanation of the algorithm. If they can be resolved satisfactorily, I would be happy to revise my recommendation.
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_of_paper: The paper frames the robot obstacle avoidance problem from the viewpoint of online learning. The policy adapts online to uncertainty and is shown in hindsight to be provably comparable to the best obstacle avoidance policy (from a restricted class). The method is validated in simulation and compares favorably to baselines (open loop planning and Hamilton-Jacobi reachability). It is also implemented on a hardware example. 
review: Strengths: The paper makes a novel technical contribution in that it a. poses the obstacle avoidance problem in a regret minimization framework and b. develops a trust region-based online learning algorithm for obstacle avoidance with provable bounds. The paper is clearly written and easy to understand. 

Weaknesses: Modern robots have many failings. However, running into things is not high on the list. When we live in an era of driverless cars that have driven on public roads for millions of miles without a collision, it is questionable whether stationary obstacle avoidance is a significant enough problem to warrant an attack. I am (largely) on board with this paper in terms of its intellectual contributions, but I am not convinced it is solving a significant or important problem. A further limitation is that the comparisons are made with a narrow class of baselines and only in a setting where the obstacles are stationary. 

robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This paper has some nice aspects but is not significant enough to be published at CoRL.
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 an algorithm for robot navigation with obstacle avoidance. 
The algorithm is robust against uncertainties and perturbations and adapts online, also to local obstacle sensing.  
Theoretical results are provided and extensive simulation and experimental results are conducted. 
review: The topic addressed is significant and very relevant to robotics and the proposed algorithm seems promising.
The paper adapts an algorithm combining previous works ([38]+[52]) for obstacle avoidance. Therefore, the theoretical contribution needs to be clarified here - this is also due to the fact that it is not explained what exactly these previous works do. 

The related-work part does not seem complete; there are more works than ones that probabilistic model - based and worst-behaviour-based. For example, the adaptive control works by Dimarogonas group: 

Verginis and Dimarogonas. "Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics." Automatica, 2021.
Verginis, Dimarogonas, and  Kavraki. "Kdf: Kinodynamic motion planning via geometric sampling-based algorithms and funnel control." IEEE Transactions on robotics, 2022.

that adapt online to uncertainties and disturbances. 

The proposed algorithm further needs some clarification. For instance, what exactly tilde{M} is and how are the corresponding x and u (with tilde{M} superscripts) rigorously defined. Is the Initialize procedure in Alg. 1 executed offline in some simulation environment? Because it requires runs of the system to observe the several properties.    

Similarly, some more details throughout the paper can be provided, such as how is (4) exactly derived from (1)-(3), what exactly is the FPL methods, or what [38,52] consider.

References to the supplementary material should be avoided (like see Supp. B in Theorem 2) and the picture references in the experiments. 

I understand the space limit (also with respect to my comment on more clarifications) and that a lot of information is in the appendices, but the paper needs to be compact in that sense. 








 
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I believe the paper presents a strong algorithm for navigation and collision avoidance, but modifications are needed based on contribution, literature, and clarity.
review_title: null

Meta-review: Scores:    DrfS: Weak Accept   x84o: Weak Reject,   63fg: Weak Accept

Quality: The paper presents an online learning algorithm for obstacle avoidance in robot navigation. The resulting policy is robust against uncertainties and perturbations and  in hindsight is provably comparable to the best obstacle avoidance policy (from a restricted class). Theoretical results are provided and extensive simulation and experimental results are conducted. All reviewers  have acknowledged that the paper presents a strong algorithm and is well executed.

Clarity: The paper is clear and well organized.

Originality: Good. The paper makes a novel technical contributions by formulating  the obstacle avoidance problem in a regret minimization framework and by developing a trust region-based online learning algorithm with provable bounds, which  is important for safety considerations.

Significance: As online obstacle avoidance algorithm with guaranteed safety bounds are rare, this paper provides a significant contribution.

Cons:

- the significance of the provided method in comparsion with other more data-driven, obstacle avoidance algorithm is not entirely clear

Pros:

- adaptive online obstacle avoidance algorithm (including dynamic obstacles) with policy safety certification (regret minimization)
- algorithm is robust against uncertainties and perturbations
- theoretical results are provided and extensive simulations and hardware experiments are conducted
Decision: Accept (Poster)