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Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster |
Soundness: 2: fair
Presentation: 4: excellent
Contribution: 2: fair
Rating: 6: marginally above the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper discusses the addition of muscles to the NeuroMechFly drosophila body model. 15 muscle-tendon units were added to each of the forelegs, based on micro-CT imaging. The authors used Hill-type muscle models, and tuned the parameters to reconstruct captured tethered walking and grooming data. They then used imitation learning to learn policies to perform walking and grooming behavior. They also analyzed muscle synergies and examined the effect of passive forces on the policy learning process.
1. The paper contributes a new component to a whole-body model of the fruit fly: anatomically realistic leg muscles.
2. The authors integrate anatomical imaging data, behavior data, muscle and joint dynamics modeling, and imitation learning in an interesting way. Their pipeline seems like a reasonable starting point that others can learn from, especially with respect to selecting which muscles parameters to optimize and the actual process of optimizing them.
3. The paper is well-written and the results are presented clearly.
1. The model the authors present only has muscles in parts of the front two legs, and the work doesn't deal with ground contact or external forces, which could make it difficult to directly apply when modeling many behaviors.
2. It's a little hard to tell without frame-by-frame comparisons, but it seems like the reconstruction quality presented in panel 3c might not be that high, at least for walking data.
3. I'm not sure what to make of the muscle synergy analysis. If a meaningful set of synergies was really discovered, I would expect to see a knee in panel 4c, but it doesn't seem like there is one. The explained variance numbers are a little hard to interpret as well; it might be more interesting to know how the fit quality would degrade using 1-, 2-, or 3-dimensional control vectors (with learned per-muscle weights) instead of controlling each muscle independently.
4. The motivating claim on line 451, "Placing a model of the musculoskeletal system—an additional layer of processing—between the policy network’s output and physical actions stabilizes the control task by making the action space better formed and more error-tolerant", does not seem to be substantiated. To support this claim, I think the authors would need to compare policy learning in models with and without muscles and per-joint passive forces.
5. Unless I missed it, there isn't a direct comparison to the original version of the NeuroMechFly model the authors are extending. This would probably help contextualize the behavior fitting results.
1. What exactly are the inputs to the static optimization procedure used to fit muscle parameters? Model structure + instantaneous joint positions, velocities, and accelerations? And what is the loss function?
2. Why does panel 3c use arbitrary units? Either degrees or (unitless) joint range fractions would be easier to interpret.
3. The plot labels in panel 3d might be a little confusing to some readers. I think I eventually figured out the shorthand, but it took me a minute.
4. On line 381, what is meant by "systematic evaluation"? Do the curves plotted correspond to the best models after discovered via a grid search, in which case shouldn't the red-orange family be expected to have an advantage, since there's a larger space to explore?
5. Where did the reference angles for the joints come from? Would it be worth attempting to discover better values for these?
6. In terms of reproducibility, it looks like the tracked keypoint locations are included in the supplementary material, which should be helpful to readers interested in replicating the authors' results. Are the behavior videos and/or the imaging data available as well, or will they be made available? |
Fully human-written |
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Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster |
Soundness: 4: excellent
Presentation: 4: excellent
Contribution: 4: excellent
Rating: 8: accept, good paper
Confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully. |
This paper presents a 3D musculoskeletal model of drosophila legs. They propose a data-driven approach to building these models and unknown muscle parameters are estimated by an optimization approach, quite similar to system identification techniques. Once these detailed muscle models have been built, the authors present results on muscle activation patterns, muscle synergies and the effect of passive joint properties like damping, stiffness on muscle activates using Reinforcement Learning technique to mimic recorded drosophila leg movements for two distinct tasks - locomotion and grooming.
The paper is very well written, structured and easy to read. The level of detail, extensive experiments and the thoroughness of the evaluation presented in this paper needs to be appreciated. The problem statement is well motivated and its relation to existing research clearly presented.
The visualizations used in the paper, both of the muscles as well as quantitative results, are great and are really helpful for a reader to understand the text better.
The results presented in the paper are very insightful and come from well thought out experiments. The discussions about muscle synergies and effect of passive joint properties will definitely inspire future research.
While the results are promising, I found that certain details make the descriptions in the paper confusing. For example, All through section 3.2 and 3.3, one of the tasks being described are locomotion and most readers will have a mental image of a drosophila walking. But, in the limitations and future work section, it’s mentioned that body-body and body-environment contact forces are ignored - does this mean that the trajectory following was done without any contact between the leg and a surface? Adding to the confusion - In the appendix - the authors mention “We concentrated our efforts on developing a fully functional front-leg muscle model for two main reasons”. Does this mean that only the front two leg motion were simulated in the experiments for locomotion? Clearly describing the exact task would be very helpful to the reader.
Some details about imitation learning would be a great addition to the paper - what was the dimensionality of the state and action space?
For most muscles in figure S6, the learned activations seem to be either 0 or 1 across all passive joint properties. Can the authors comment on the plausibility of this in a real musculoskeletal system?
Typically for reward functions in reinforcement learning for locomotion or any in general imitation of a recorded trajectory - there is a term that penalizes effort. For example, sometimes this can be minimizing joint torques, accelerations, muscle activations, etc.. I'm curious if this was considered during experiments?
Im also interested in the authors thoughts about the relationship between muscle synergies and passive joint properties, would it make sense to experiment how the synergies changes as joint properties change?
Comment - Typically exploration in reinforcement learning is challenging, especially when the action space is muscle activation. There has been some work in addressing this - https://openreview.net/forum?id=C-xa_D3oTj6 . The idea is not to naively explore but to identify some state dependent exploration that helps learning efficiency. This could be a useful tool for next set of experiments using reinforcement learning. |
Fully human-written |
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Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster |
Soundness: 3: good
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
The authors of this manuscript developed a pipeline to estimate the properties of leg muscles for simulation directly from X-ray scans. They then applied them to imitation learning experiments to make predictions about muscle synergies employed across different behaviors, and investigate the influence of passive joint properties on learning.
The muscles in musculoskeletal modeling are a much-needed part of understanding the neural control of movement. This has been sorely lacking in biomechanical simulations of Drosophila, but this paper aims to address that gap.
1. The details of their optimization-based approach to estimating muscle parameters is interesting, since experimentally estimating these properties is challenging.
2. While the results about muscle synergies being different across behaviors are not surprising, the investigation of passive joint properties effect on learning is a clever utilization of MuJoCo joint parameters to address a scientific question about the advantage of using a combination of passive mechanics and active control.
1. The result that distinct synergies are employed across behaviors (walking vs. grooming) aligns with well-known concepts. While this confirms the model’s biological plausibility, it does not yield surprising scientific insight.
2. Environmental contact forces and interactions are excluded, restricting the ecological validity of locomotion simulations.
3. Although the optimization framework is solid, the lack of direct experimental validation (e.g., comparison with EMG) limits confidence in the accuracy of the predicted activations and synergies.
4. The topic is very niche.
See weaknesses above. |
Fully human-written |
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Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster |
Soundness: 3: good
Presentation: 3: good
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper constructs a foreleg musculoskeletal systems of fly based on anatomical data and optimization. The authors model 15 muscle-tendon units of each leg based on two public and one custom anatomical dataset, and optimize the muscle parameters to align with fly joint movement on OpenSim. They also convert the OpenSim model to the Mujoco platform, and augment the model stability by adding passive biomechanical forces. Model analysis demonstrate the qualitative accuracy of the fly musculoskeletal model in OpenSim, and the converted Mujoco model with passive forces is capable of imitating fly motion data.
1. The proposed model is the first musculoskeletal model of the fly, which requires substantial effort for the anatomical data. The model provides a platform for better biomechanics and neuroscience research.
2. The whole model building pipeline is detailed, which might help the musculoskeletal modeling of other animals.
3. The paper is clear and well written.
1. This model only has foreleg muscles and ignores all the contacts, which cannot achieve common behaviors such as locomotion and manipulation.
2. The whole model building, optimization and analysis is conducted in OpenSim, but the musculoskeletal control is conducted over Mujoco. The Mujoco model has passive biomechanical forces where the OpenSim model does not have. It is not clear whether the muscle accuracy and synergies result in Opensim can be transfered to a different platform.
3. In figure 3c, the RMSE of coxa-trochanter is large with low correlation. Video results on the tracking performances over both OpenSim and Mujoco might better help assessing the model building fidelity.
1. In figure 3c and Figure 4d, what does (au) means in the y axis label?
2. In figure 4a, it would be better to show the reference joint trajectory compared against the joint movement generated by OpenSim static optimization.
3. How to determine the parameters of passive forces in Mujoco? |
Fully human-written |