Venue: CoRL 2022 Conference

Reviews:

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: The paper considers risk estimation of predictors used by planners for robots. Robustness of plans is achieved by pushing the risk estimation to prediction, rather than computing it at planning time. This has two advantages: (a) planners do not need to be updated when considering new predictors and risk measures; and (b) it simplifies planning using expectations over biased (risk-aware) distributions. The paper shows the approach on didactic and real-world datasets.
strengths_and_weaknesses: Strengths
+ The paper is clear and well written.
+ It has good explanations and arguments for all design decisions and theory.
+ The literature review and framing is good.

weaknesses
- An explicit contributions statement at the end of the introduction would strengthen the paper.
- The paper could highlight the significance and benefits of the "simplification" more, e.g., what is the impact on runtime performance at deployment.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper introduces an approach and ideas with significant potential impact on robot deployment. It is well written and technically sound. The results are shown empirically on small (didactic) and real-world datasets.
issues: In Sec. 5.2, what is $s$? Should $s = \sigma$ or is $s \geq 1$?
Moreover, the penalty $\rho(x)$ is discontinuous in $x$. How does it impact performance? Also, is the problem well posed?  Would $s \cdot (1 + \log(x) )$ work instead of $\log(sx)$?
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 to learn a risk-aware prediction model. The key idea is to learn an extra encoder model of the latent random distribution minimizing a cost function biasing the latent random variable to generate risk-biased trajectories. The article makes two main contributions:
1 - a risk-aware prediction model
2 - a novel training objective to learn a risk-biased distribution of the latent random variable
strengths_and_weaknesses: First, this paper tackles an extremely challenging and important problem to the robotics community. Secondly, the proposed idea is interesting and the proposed approach novel. Overall, the paper is well written, the method is described clearly and the presented results support the claimed contributions.

However, this paper has the following main limitations
1 - It only considers simple scenarios with a maximum of 2 agents
2 - the risk-aware prediction do not depend on the ego's plan

Therefore, in future work and in a journal of this work I recommend the authors to:
1 - Consider complex scenarios with a high number of agents.
2 - Evaluate and discuss how this approach would scale to higher number of agents
3 - Will over-conservative prediction lead to the freezing-robot problem? How to address this issue?

The presented results can also be strengthened:
1 - Present more qualitative results in the form of a video to clearly show the how the predictions vary with the risk value
2 - How computationally demanding is to train such model? What is the training time? How much time it takes on training iteration? 
3 - How does this model generalizes to new unseen scenarios?

Finally, I recommend the author to revise their citations. It is good that they did such broad literature review but there are several sentences where the citation does not support the written sentence.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: This paper tackles and important problem and it proposes an interesting new method. The presented results only consider very simple scenarios but I am ok with those for a conference paper as they clearly demonstrate the advantage of the proposed approach. Adding more experimental results, in a video format for instance, would improve the contribution of the article. Moreover, the limitations section needs to address more issues of the proposed approach. Overall, i think it is a good paper, well written and it has a clear contribution which the results support. Therefore, I recommend its publication.
issues: Presented results only consider simple scenarios with only 2 agents.
Therefore, in future work and in a journal of this work I recommend the authors to:
1 - Consider complex scenarios with a high number of agents.
2 - Evaluate and discuss how this approach would scale to higher number of agents
3 - Will over-conservative prediction lead to the freezing-robot problem? How to address this issue?
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: There are several recent works that aim to enhance robustness of planning against various sources of uncertainty. This is typically accomplished by  designing risk-aware planning methods. The issue in these methods is that is risk is approximately computed which may lead to unsafe behaviors. This paper follows a different approach and instead of robustifying planning, it robustifies prediction by making it risk-aware. Particularly, a new prediction objective is proposed to learn risk-biased distribution over trajectories of agents that exist in the environment. This approach reduces sample-complexity during online planning which facilitates real-time implementation. The proposed method is supported by simulation results. 


strengths_and_weaknesses: Strengths:

The idea of proposing a risk-biased trajectory forecasting framework is quite interesting and novel. This pessimistic forecasting allows the planner to be distributionally robust, i.e., robust to un-modeled sources of uncertainty.

The proposed method does not require modification of the planner and in fact it seems that it can be integrated with various planners (e.g., risk-aware planners and risk-neural planners).

A biased distribution  is constructed from which samples are drawn to forecast future states of agents. This biased distribution allows to accurately predict future states with only few samples.

Weaknesses

The evaluation is somewhat weak. It should be improved to demonstrate more clearly the distributional robustness of the proposed method. 



robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, the authors propose a novel and quite interesting approach to robustify predictions. The proposed risk-aware forecaster can be coupled with existing planners, whether they are risk-aware or risk-neural, and it can design safer (but more conservative) paths than existing risk-sensitive planners. The developed algorithm is supported by nice simulation results. Nevertheless, the evaluation should be improved to demonstrate distributional robustness in a more convincing way.


issues: The authors should address the following issues:

- The evaluation needs to be improved to demonstrate distributional robustness in a more convincing way. For instance, what happens if the human behaves differently between training and test time (section 6.2)? In this scenario, how does the proposed method perform and also how is its performance compared against risk-sensitive planners?

- Figures demonstrating the robot trajectories generated by the proposed method as well as competitive approaches would be helpful (Section 6.2). In fact, this would help visualize how conservative the designed paths are.

- In the simulation studies, the TTC metric needs to be defined. Also, how are the prediction samples defined (e.g., in Fig. 3)? Do they refer to sample trajectories using to train the predictor?

- In the abstract, the authors state :"This reduces sample complexity during online planning for safe real-time performance". This statement is confusing. How is sample complexity related to online planning? Samples are collected offline to train a risk-biased predictor. Given, this trained predictor, a planner is used online to design trajectories. Even if the offline training process is not sample efficiency, this has nothing to do with online planning, since samples are not used online for training. The authors should re-write this sentence more clearly.
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 tackles risk sensitive planning for vehicle prediction. Existing approaches often fail make diverse predictions suitable for risk aware planning. Additionally, adapting an existing risk-neutral planning pipeline to risk aware one may be costly. 

Instead of targeting downstream risk-sensitive control, this paper encodes risk into upstream prediction components. The authors' key insight is that a risk sensitive measure of cost is the same as the risk-neutral expectation of cost under a distribution of trajectories with risky trajectories up-weighted. This means they can use VAEs to learn an importance sampler which biases towards risky parts of the trajectory space, implicitly learning a risk-sensitive measure via proxy.

Experiments are given on a small pedestrian proof of concept scenario, as well as larger experiment on the waymo open dataset. They demonstrate usefulness of their method as compared to an unbiased CVAE. 
strengths_and_weaknesses: ## Strengths

- The paper is easy to follow, with good mathematical layout and comprehensive existence proofs accompanying each of the measure formulae.
- The contribution of the paper itself is a novel synthesis of Variational Autoencoder methods, Cross-Entropy importance sampling, and risk sensitive measures. This combination provides a valuable tool for adding robustness to the predictive layers of an perceptual-control pipelines.
- The results and figures given (in particular, the histograms of sampling for the biased vs unbiased experiments) do a good job showing the intuitive operation of their algorithm, and how it effectively biases predictive distributions towards risky parts of the space.

## Weaknesses

-  There is an odd statement towards the end of section 4 about proving that the solution to their optimization problem is unique. This is under the assumption that the decoder is "Volume Preserving" and they even provide a citation for a method for implementing volume-preserving neural networks. However, they then state that they do not attempt to meet this assumption, yet still present it as evidence their problem is well posed. I'm not sure I follow this! I think for the statement to be of any value, I would as least like to intuitively understand what it would take to bring their method in line with the volume preservation assumption.
- In Section 5.2, there are a few user-defined constants ('s', 'alpha', 'beta'). It would be nice to see some analysis of how altering these values affects the end result.
- The work is well situated within most of the related literature it deals with. However, the methods used also draw heavily on importance sampling and rare-event validation. I would have liked to see more engagement with this work. For instance, how does the work fit alongside things like (https://ieeexplore.ieee.org/abstract/document/7933977) or (https://proceedings.neurips.cc/paper/2018/hash/653c579e3f9ba5c03f2f2f8cf4512b39-Abstract.html)? Additionally, how do their methods relate to concepts like the proven ideal optimal zero-variance sampler from the importance sampling literature?
- The authors mention in their limitations section that, while their method works for risk-aware planning at the 95% level, it may not be suitable for much rarer events. I agree, but I think this point is understated: For the AV domain in which their experiments take place, *extremely rare collision events are, in fact, the norm*, with safety requirements several magnitudes stronger than the ones given in the paper. 
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: On the basis of the strengths and weaknesses outlined above, I think this is a good candidate for publication. My suggestions for additional analysis and literature are minor issues, stemming from a desire to see the work expanded, rather than anything preventing the work from achieving publication quality.
issues: No major issues. As mentioned in the strengths/weakness section, any insights you could provide into how this work fits into the context of existing rare-event sampling literature, and intuitions about how some of the hyper-parameters in the experiments section affect performance, would help broaden my understanding of the work.
review_title: null

Meta-review: metareview: Summary: 
This work builds a risk-aware prediction model. To construct risk-biased trajectories, it learns an additional encoder model of the latent random distribution by minimizing a cost function biased by the latent random variable.   This avoids the need to update planners when new predictors and risk measurements are evaluated and simplifies planning utilizing expectations over biased (risk-aware) distributions. The paper uses didactic and real-world datasets to explain the approach.  This novel prediction objective learns risk-biased agent trajectories. This strategy decreases online sample complexity for real-time implementation.  Experiments on a pedestrian proof-of-concept scenario and the Waymo open dataset are presented and shows an improved performance over unbiased CVAE. 

Strengths 
1.	The concept of developing a risk-biased trajectory forecasting framework is intriguing and novel. This forecasting technique enables the planner to be distributionally robust, that is, resilient to unmodeled sources of uncertainty. 
2.	The proposed method does not necessitate any changes to the planner, and it appears that it can be integrated with a variety of planners (e.g., risk-aware planners and risk-neural planners). 
3.	To forecast future states of agents, a biased distribution is constructed from which samples are drawn. With only a few samples, this biased distribution allows for accurate prediction of future states. 
4.	The paper is simple to read, with strong mathematical design and extensive existence proofs accompanying each of the measurement formulae.  
5.	The paper's contribution is a unique synthesis of Variational Autoencoder methods, Cross-Entropy importance sampling, and risk sensitive measures. This combination is a valuable tool for enhancing the predictive layers of perceptual-control pipelines. 
6.	 The results and figures provided do a good job of demonstrating the algorithm's intuitive operation and how it effectively biases predictive distributions towards risky parts of the space.


Weaknesses
1.	A limited number of agents is considered – More sophisticated, multi-agent scenarios should be considered and a discussion on how this will scale to more agents should be in the paper. 
2.	It is not clear how to handle robots failing due to the simplified prediction approach. 
3.	A discussion on how generalizable the method is was not done. 
4.	The citations need to be reviewed and updated due to the numerous sections in the paper requiring references. 
5.	It is not clear what the usefulness of the volume preserving decoder is.
6.	Reviewers have listed some related work that has not been discussed. 

confidence: 5: The area chair is absolutely certain
Decision: Accept (Oral)