From: graphicsinterface.org/Graphics_Interface/2023/Conference/Paper10/Area_Chair_Laf7
metareview: The reviewers generally agree that the paper presents an interesting control framework based on bio-inspired SNNs, which has not been widely explored in graphics and animation. The audience of GI may find this topic inspiring. 

However, the creatures and behaviors studied in this research are less challenging than those already shown in previous works. There are also concerns regarding the comparison with RL approaches and whether simpler control strategies can accomplish the same task.

It is recommended that the authors reconsider their discussion regarding the advantage of their method over RL and kindly consider the other suggestions in the reviews in revising the paper.
recommendation: 5: Marginally below acceptance threshold
confidence: 5: The area chair is absolutely certain

From: graphicsinterface.org/Graphics_Interface/2023/Conference/Paper10/Reviewer_nwnv
title: Interesting topic, but with limited technical significance. Some arguments are not convincing.
review: 
The paper presents a control system built with spiking neurons that achieves the crawling and swimming behaviors of amphibious creatures with articulated skeletons. The paper designs several spiking network architectures that generate coordinated control signals for the creatures' bodies and limbs. Combined with simple feedback strategies, these networks allow the virtual creatures to move and accomplish simple tasks such as collision avoidance.

The technical contribution of the paper is the design of the spiking networks, which may inspire future works in relevant directions. However, I am not entirely convinced by the discussion regarding the advantage of the proposed method, including:

It is argued that the proposed method "alleviates the need for training." However, the proposed system heavily relies on manually designing the network structures. It can be hard to generalize to complicated tasks such as biped locomotion. Even for amphibious creatures, it is not clear how to easily create different types of movement using the proposed system. I would believe that training is still necessary for such complicated tasks. It is not convincing to say that "no need for training" is an advantage of the proposed system. It is not fair either to compare the manually crafted policy with RL training in terms of computation cost.

The argument that the proposed network can be potentially more energy efficient than existing systems is also questionable. For virtual creatures, the proposed system needs to simulate a population of spiking neurons to approximate the desired signal trajectory, which seems much more computationally expensive than other approximation functions, such as splines. For real robots, although it is generally agreed that spiking neurons are more energy efficient than linear neurons, whether such energy saving is significant compared with the power consumed by the motors is not clear.

Overall, the paper does have some interesting aspects. However, I do worry about its generalizability to more complicated motions, and I am not fully convinced by several arguments. For this reason, I think the paper may not be ready for publication yet.
rating: 4: Ok but not good enough - rejection
confidence: 3: The reviewer is fairly confident that the evaluation is correct

From: graphicsinterface.org/Graphics_Interface/2023/Conference/Paper10/Reviewer_yRix
title: Review of Bioinspired Dynamic Control of Amphibious Articulated Creatures with Spiking Neural Networks
review: The paper proposes a method to control amphibious articulated creatures with Spiking Neural Networks (SNN).  Three types of networks are proposed to produce different type of behaviors.  the sequential alternation network that gives rise to alternating, synchronized contractions of the right and left sides of the creature’s body, (b) the chained oscillator network that gives rise to an undulatory movement of the spine by propagating the activation of the joints along the spine, and (c) a subnetwork that is used in conjunction with the sequential alternation controller to move the creature’s leg.   These networks form a feedforward controller that produces open loop movements.  Various motions such including swimming and crawling can be successfully generated by the system.  Also, by introducing a feedback controller based on a GPS and distance sensing, that adjust the spiking signals for the SNN, avoidance motion and transition motion can be successfully generated.  The speed of the locomotion can be also controlled by lowering/increasing the amplitude of the spiking patterns.  The animation appears natural and realistic.  The results are compared to PPO-based RL controllers in MUJOCO OpenAI gym.  Although the motion by the RL controller eventually exceeds the SNN-based controller, it requires more than 110k episodes to reach the level of the SNN.  It is not written what the reward of the RL is.  
 
The paper is well written and easy to go through.  The video is helpful for understanding the methodology and the quality of the animation that can be generated by the method.   I am not an expert in the area of bioinspired robotics or biology so it is difficult to judge the novelty of the model from the science point of view.  I have seen various models as the proposed model in human locomotion such as those by Gentaro Hase, Kazunori Hase etc., and such movements are far more complex than the swimming behavior and crawling motion of amphibious articulated creatures who do not keep the balance during locomotion.   In comparison to the RL controller, the authors insist that the bio inspired model does not require training, but the model requires manual tuning of the parameters and the network structure for producing good looking motion so I am not sure if such a comparison is very fair.   I thought it will be interesting to learn the parameters of the bioinspired model by RL but these is no mention about that in the future work. 

Overall, the paper is well written and can also inspire graphics and animation researchers for doing bio-inspired animation of characters, but the novelty of the paper is not very clear to me - I assume the three types of networks are not very novel as such structures should be well known in biology.  For these reasons, I am giving a marginal acceptance rating.  




rating: 6: Marginally above acceptance threshold
confidence: 3: The reviewer is fairly confident that the evaluation is correct

From: graphicsinterface.org/Graphics_Interface/2023/Conference/Paper10/Reviewer_F6Xr
title: The paper present an interesting controller for oscillatory behaviours focusing on swimming, crawling, and quadruped walking. The controller is based on Spiking Neural Networks, which are used to miimic the output of CPGs.
review: The paper is well written. The method and the evaluation are clearly described. It proposes  biologically inspired controllers for the purposes of oscillatory movement such as swimming, crawling, and quadruped locomotion. The output of spiking neural networks is used to approximate CPGs that drive the degrees of freedom of the character. Through parameter exploration, hand designed adaptations allow the creatures to alter their speed and direction, avoiding obstacles. The results demonstrate that the method works as advertised.

The limitations of the method are clearly discussed. I also appreciate that the authors are willing to release their code upon publication. The video was very informative.

I think that this is an interesting method. However, it is not clear to me if it can generalize to more complex behaviours that required active balance,  and complex sensing, such as the movement of crowds of humans. As the method is presented, it provides an interesting implementation of SNNs, and insights on their biological counterparts. However, in terms of applicability, there are earlier methods that have shown very successful swimming and snake like motion such as the seminal paper on artificial fishes by Tu and Terzopoulos, and subsequent work.

I think it would be great if the authors discussed whether one could by pass the SNNs and use directly sinusoidal functions to control the DOFs of the creatures. 

Overall it is an interesting read, and it might be of interest to the audience of GI 2023.
rating: 6: Marginally above acceptance threshold
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct