Venue: ICLR 2023 Workshop RRL

Reviews:

final_score: rating: 3: Accept
review_content: **Summary:**

The paper studies trajectory optimization using diffusion models building on the methodology of [1].
The main contribution is an extension of the diffusion models allowing equivariance to the SE(3) x Z x S_n group and using it for planning in environments with relevant symmetries.
The main results are two-fold: first, authors show that a model with equivariance outperforms the non-equivariant one in the limited data regime. 
Second, the equivariance property allows better generalization in cases where all training trajectories are oriented in a single way.
All experiments use a 3D navigation task and a simulated robotic manipulation environment.


**Quality:**

The design decisions for extension are well-justified and seem correct.
While none of the individual components of the proposed approach is novel ([2] extends diffusion models to product groups T^3 x SO(3) x SO(2)^m, [1] uses diffusion models for planning), their combination is novel.


**Clarity:**

The paper is well-written and contains all the necessary background and experimental details.


**Significance:**

The paper uses the offline RL setting, but beyond that, the relevance to the workshop is limited.
The paper also does not give many new insights: the results mostly confirm previous findings about equivariance being helpful for sample efficiency if environment symmetries can be exploited [3] and that equivariance can lead to generalization from restricted data [4].


**Overall recommendation:**

The reviewer recommends acceptance to the workshop as the paper provides a valid extension of previous works.


[1] Janner, Michael, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine. "Planning with diffusion for flexible behavior synthesis." arXiv preprint arXiv:2205.09991 (2022).

[2] Corso, Gabriele, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola. "Diffdock: Diffusion steps, twists, and turns for molecular docking." arXiv preprint arXiv:2210.01776 (2022).

[3] Van der Pol, Elise, Daniel Worrall, Herke van Hoof, Frans Oliehoek, and Max Welling. "Mdp homomorphic networks: Group symmetries in reinforcement learning." Advances in Neural Information Processing Systems 33 (2020): 4199-4210.

[4] Simeonov, Anthony, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann. "Neural descriptor fields: Se (3)-equivariant object representations for manipulation." In 2022 International Conference on Robotics and Automation (ICRA), pp. 6394-6400. IEEE, 2022.
review_title: An equivariant extension of the Diffuser

final_score: rating: 4: Good paper, strong accept
review_content: In this paper, authors proposed EDGI, method based on diffusion framework, which is equivariant with respect to the product of the spatial symmetry group SE(3), the discrete-time translation group ℤ, and the object permutation group 𝕊ₙ. Paper is well written (with good visuals for the main architecture), all the details are explained clearly (even for readers who are not familiar with the topic, as I am) and all the claims are aligned with the actual results. While the results in standard setting are on par with Diffuser, EDGI is superior in the generalization setting and has greater sample efficiency in low data regime. Thus, I believe that this work is a novel and valuable contribution. 

In addition, I think it would be helpful to also add a comparison of inference and learning times versus baselines, so that the reader can understand how much overhead gives equivariance, as usually in robotics there are quite strict limitations to the inference speed.
review_title: interesting and valuable addition to the family of diffusion models in the offline model-based RL

Decision: Accept(Poster)