Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model

John R. Hutchinson; Leonard Hasenclever; Steven Bohez; Vittorio La Barbera; Yuval Tassa

REVIEW 4 major objections 5 minor 36 references

Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A 133-muscle dog model built automatically from 3D muscle meshes tracks motion-capture clips and produces muscle activation timings that agree qualitatively with published canine EMG recordings.

desk verdict Useful new dog model and muscle LoA pipeline, but the EMG validation rests on unmeasured muscle strengths (F0) and needs an ablation before the science holds up. read the letter →

arxiv 2506.23768 v2 pith:SJHHP44N submitted 2025-06-30 cs.RO

Vittorio La Barbera , Steven Bohez , Leonard Hasenclever , Yuval Tassa , John R. Hutchinson This is my paper · ORCID

classification cs.RO

keywords musculoskeletalmodelcaninelocomotionmotioncapturetrackingmuscledynamicslineofactionextractionpredictivecontroliLQGelectromyographycomparison

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that a biomechanically plausible quadruped model can be built largely automatically from anatomical 3D muscle meshes, and that such a model can be driven by gradient-based predictive control to reproduce real dog movements. The authors construct a 133-muscle dog model by slicing each muscle mesh and connecting the slice centroids into a line of action, then add a motion-capture tracking task. They replace the standard muscle model's hard activation/deactivation switch with a smooth sigmoid, which makes the dynamics differentiable enough for the iLQG controller to track walking, sprinting, sit-to-stand, and jumping clips. The simulated muscle excitation patterns are then compared with published canine EMG data, with broad qualitative agreement for most of the ten muscles examined. If the claim holds, the work offers a reusable pipeline from anatomical geometry to controllable simulation for studying muscle function without new animal experiments.

What carries the argument

The central machinery is a three-part pipeline. First, line-of-action extraction: each muscle mesh is sliced along its longitudinal axis at intervals proportional to muscle length; the centroid of each slice becomes a candidate via-point, kept when the path moves to a new bone or the gap since the last kept point exceeds a distance threshold, producing anatomically reasonable tendon routings without manual placement. Second, differentiable muscle dynamics: the Millard activation model's hard switch between activation and deactivation time constants is replaced by a sigmoid interpolation modulated by a smoothness parameter, so the activation dynamics are smooth enough for iLQG. Third, the motion-tracking task: a weighted cost of joint velocity, muscle excitation, marker-position error, and marker-velocity error is minimized by iLQG within the MuJoCo Predictive Control framework, converting raw motion-capture marker trajectories into muscle excitation signals.

What would settle it

Repeat the walking clip with per-muscle peak forces recomputed from published canine PCSA and fibre-length values; if iLQG no longer tracks the motion, or if the gastrocnemius and triceps activation phases stop matching the EMG reports, then the reported EMG agreement depends on the unvalidated scaling rather than on the model's anatomy.

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Extended reading notes

Core claim

The paper's central claim is that a full musculoskeletal quadruped model does not have to be hand-tuned muscle by muscle: cutting each 3D muscle mesh into slices, connecting the slice centroids into a tendon path, and adding the resulting 133 actuators to a dog skeleton yields a model that a gradient-based predictive controller can drive through real motion-capture clips. The enabling modification is replacing the discontinuous Millard activation/deactivation switch with a sigmoid-smoothed transition, which gives iLQG a usable gradient. With this change, the controller tracks walking, sprinting, and sit-to-stand clips with marker errors within about 0.4 meters, and jumping with errors up to about 1 meter. The resulting excitation signals reproduce the phase patterns reported in canine EMG studies for most of the ten muscles compared, with the authors flagging specific exceptions such as vastus lateralis during walking, flexor digitorum superficialis during sit-to-stand, and notably high biceps brachii activity during jumping.

Load-bearing premise

The load-bearing premise is that the maximum force assigned to each muscle, which was taken from the simulator's built-in scaling rather than from measured dog physiology, is close enough to reality that the simulated activation timings can meaningfully be compared with real muscle recordings.

Editorial extensions

If this is right

The same pipeline can be rerun from fresh muscle meshes to produce a controllable musculoskeletal model without manual muscle-path authoring, so other dog breeds or other quadrupeds become practical to model.
The smooth activation dynamics remove the main gradient barrier that kept iLQG from controlling 133-muscle models, making model-based predictive control a viable alternative to reinforcement learning for musculoskeletal tracking.
Simulated excitation timings can serve as testable predictions for muscles and gaits where experimental EMG is missing, and the planned release of the model and 28 motion-capture clips lets other groups reproduce or extend those predictions.
Tracking marker positions within about 0.4 meters for walking, sprinting, and sit-to-stand suggests the model can be used to study muscular demands of everyday behaviours, while the larger jumping error marks jumping as a stress test requiring better force or contact modelling.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the per-muscle peak-force constants were taken from the simulator's built-in scaling rather than validated physiology, a sensitivity analysis sweeping those constants per muscle would reveal which of the reported EMG matches are stable and which depend on that scaling choice.
The validation is qualitative and marker-based, so the same model could be checked against force-plate ground reactions and joint moments; those data would test whether the kinematics are achieved with realistic kinetics, not just realistic-looking paths.
The sigmoid-smoothed muscle dynamics are not specific to dogs: applying the same smoothing to other MuJoCo muscle models could make iLQG practical for other high-dimensional musculoskeletal systems and could also ease gradient-based reinforcement learning.
If the F0 scaling can be tied to published physiological cross-sectional areas and fibre lengths, the tracking setup could be inverted into a tool for estimating muscle forces during locomotion, not just activation timings.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

I'll cut to the chase: this is a useful engineering contribution that overreaches one scientific claim. The new things are real: a 133-muscle MuJoCo dog built from 3D meshes, a simple centroid-slicing line-of-action extraction, a sigmoid-smoothed muscle activation dynamics, and a new Border Collie mocap set. The tracking results look plausible—within 0.4 m for walk/sprint/sit-to-stand, about 1 m for jump—and the model plus data will be a resource people actually use.

The soft spots are concentrated in the validation of muscle excitations against EMG. The reader is right that the per-muscle peak force F0 is the load-bearing modeling choice. The paper states PCSA-based F0 'failed to accurately track' and then switches to MuJoCo's built-in scale, but never reports the chosen scale or resulting F0 values. Since the optimization reads out excitations, and different F0 assignments can produce different excitation patterns even with equal tracking, the claimed 'qualitative agreement' with EMG is uninterpretable without either a physiological anchor for F0 or a sensitivity analysis. That's not a minor quibble; it's the difference between a modeling artifact and a biological finding.

The other issues are proportionately smaller but real. The claim that the sigmoid dynamics is 'essential' has no ablation—show me iLQG failing with the standard Millard switch. There are no error bars or baselines for the tracking errors. The EMG comparison is honest about its mismatches (semimembranosus, vastus lateralis, flexor digitorum superficialis), which is commendable, but the mismatches are tallied with no attempt to explain them. And the GitHub link is a plan, not a release; the paper says 'we plan to release,' so reviewers can't check the model.

The stress-test note holds up; I don't see a way around the F0 issue on the evidence given. The paper deserves a serious referee—the resource is valuable and the methods are clearly described—but the revision should report F0 values, add a sensitivity analysis or PCSA-benchmarked comparison, include an ablation for the sigmoid, and release the artifacts before the EMG claim is accepted.

Who is this for? People building musculoskeletal models in MuJoCo, control researchers doing gradient-based motor control, and biomechanists who want a freely available canine platform. A reader in that group gets real value. My recommendation: send it to peer review, but expect heavy revision on the validation. I'd bring it to reading group as a good example of what a practical muscle model can do, with the caveat about F0.

Referee Report

4 major / 5 minor

Summary. The manuscript introduces a parametric musculoskeletal dog model with 133 muscles generated procedurally from 3D muscle meshes, including a line-of-action extraction algorithm and a MuJoCo implementation. To make muscle activation/deactivation dynamics compatible with the iLQG planner, the authors propose a sigmoid-smoothed replacement for Millard's discontinuous switch (Eq. 5). They retarget 28 motion-capture clips of a Border Collie and use MJPC/iLQG to track walking, sprinting, sit-to-stand, and jumping motions. The central claims are that iLQG successfully tracks these clips with the proposed muscle dynamics, and that simulated muscle excitation patterns align qualitatively with previously published canine EMG data across the three tasks. The paper also acknowledges several mismatches (semimembranosus, vastus lateralis, flexor digitorum superficialis, and high biceps brachii activity during jumping) and plans to release the model and data.

Significance. The paper addresses an important integration of detailed anatomical modeling, differentiable control, and external EMG benchmarking. If the central claims are supported, the released model and task would be a useful community resource for quadruped biomechanics and neuromuscular control. The external EMG comparison is a genuine validation step rather than a circular fit to the simulation data, and the procedural line-of-action pipeline is a practical contribution. However, the current manuscript does not yet establish the two central claims: the per-muscle force scaling F0 is unreported and unvalidated, the sigmoid dynamics is not ablated, and the tracking and EMG comparisons lack quantitative criteria. These gaps are fixable, so the contribution is potentially strong but not yet proven.

major comments (4)

[Methods, Model Creation (Eq. 2); Results, EMG comparison] The per-muscle peak force F0 is set as scale/actuator_acc0, but the manuscript does not report the chosen scale values, the resulting F0 magnitudes, or any comparison with physiological estimates. The PCSA-based alternative is dismissed only because "the model failed to accurately track the reference motion," without reporting how far those values were from expected biology. Since the tracking optimization can compensate for force-capacity scaling by adjusting excitation, the excitation patterns used for EMG validation may be artifacts of this arbitrary scaling. The authors should report F0 for all muscles, compare it with PCSA times specific-tension estimates, and provide a sensitivity analysis (e.g., perturbing F0 by plus or minus 20 percent) demonstrating that the phase relationships reported for walking, sit-to-stand, and jumping are preserved. Without these data, the qualitative EMG claim is not interpretable.
[Conclusions; Methods, Eq. (5)] The Conclusions state that modifying the model to include differentiable muscle dynamics is "essential" for accurately tracking the motion capture clips, yet no experiment is reported comparing the proposed sigmoid-smoothed dynamics with the original Millard activation-deactivation dynamics under identical cost weights and planner settings. A head-to-head ablation should report tracking error, convergence behavior, and success or failure for the same clips with and without the sigmoid. This is a load-bearing claim for the paper's stated contribution of an improved muscle dynamics model.
[Results, Fig. 6] The tracking performance is reported as average marker-position error over time, with maxima of about 0.4 m for walking, sprinting, and sit-to-stand and about 1 m for jumping. However, no error bars, number of repetitions, or clip-level statistics are given, and no baseline (e.g., kinematic retargeting without dynamics, a torque-actuated version of the same model, or chance-level error) is provided. To support the word "successfully" in the central claim, the authors should report per-clip distributions, standard deviations, and at least one baseline comparator, and should discuss whether a 1 m error for jumping is compatible with the subsequent EMG interpretation for that task.
[Results, EMG comparisons (walking, sit-to-stand, jumping)] The EMG validation is qualitative and the criteria for "qualitative alignment" are not defined. The text reports several clear mismatches: semimembranosus shows little activity, vastus lateralis is nearly constant, flexor digitorum superficialis lacks peaks during sit-to-stand, and biceps brachii is very active during jumping. The authors should provide a systematic comparison table listing, for each muscle and task, the expected phase of peak activation from the literature, the simulated peak phase, and a pre-specified agreement criterion (e.g., same gait sub-phase). This would allow readers to separate genuine agreement from selective reporting and to weigh the acknowledged exceptions.

minor comments (5)

[Methods, Eq. (5)] Equation (5) uses "sigmoid" without defining the function or its argument convention; as written, at u = a the time constant is tau_d + (tau_a - tau_d) * sigmoid(0.5), which depends on the sigmoid's scale. Please define the sigmoid and state the intended value at the switch point.
[Competing interests] The competing interests section contains the placeholder "Related patent number here if applicable," which should be removed or completed.
[Methods, Model Creation] The origin of the "leo3Dmodels" torque-actuated Pharaoh Hound model is mentioned but not cited; a reference or URL should be provided.
[Results, Fig. 6] Figure 6 should include axis labels, units, and a legend identifying the plotted clips; the current figure does not allow the reader to reconstruct the reported error values.
[Results, Sit-to-stand] In the sit-to-stand analysis, the time windows (frames 0-100, 100-300, etc.) are described only in text; annotating Figure 16 with these phases would make the comparison easier to evaluate.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the EMG comparison is externally grounded and the muscle activations are not optimized to match it.

full rationale

The paper's derivation chain is: build a 133-muscle MuJoCo dog model from 3D meshes via a centroid-slicing line-of-action algorithm; replace Millard's non-differentiable activation/deactivation switching with the sigmoid-smoothed dynamics of Eq. (5); run iLQG on the cost in Eq. (3), whose residuals penalize joint velocity, control effort, marker position error, and marker velocity error; then read out the optimal muscle excitations and compare them with published canine EMG patterns. No EMG term appears in Eq. (3), so the experimentally reported activation timing is not optimized into the controller; the comparison could fail, and the paper indeed reports several mismatches (vastus lateralis, semimembranosus, flexor digitorum superficialis during sit-to-stand, biceps brachii during jumping). The per-muscle F0 scale of Eq. (2) is a tuning choice: PCSA-based values were abandoned after tracking failure and the MuJoCo default scale is not reported. That is a reproducibility and physiological-plausibility weakness, but the EMG benchmark is not used to select F0, so no prediction is forced by construction. The self-citations (Barbera et al. 2021, Demuth et al. 2022, Ellis et al. 2018, all with overlapping authors) provide method context and an earlier simulation benchmark for sit-to-stand; they are not the source of the central activation-timing validation, which rests on external works such as Goslow et al. 1981, Deban et al. 2012, and Cullen et al. 2017. Under the stated rules, these non-load-bearing self-citations and the unreported F0 scale are correctness/reporting concerns, not circularity, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The model rests on an existing skeleton and muscle meshes from the dm_control suite, a hand-tuned LoA threshold, a global muscle scale that had to be switched from PCSA-based estimates to MuJoCo defaults, and the new tau_smooth parameter. No new physical entities are introduced.

free parameters (2)

MuJoCo muscle scale (F0 = scale/actuator_acc0) = not reported
After PCSA-based peak force estimates failed to track the reference motion, the model uses MuJoCo's default scaling; the actual scale value is not disclosed and is effectively fit to make tracking work.
tau_smooth (sigmoid smoothing time constant in Eq. 5) = not reported
Chosen by hand to interpolate between Millard activation/deactivation time constants; no sensitivity analysis is provided.

assumptions (4)

domain assumption Centroid-path line of action from 2D slices is a sufficient model of muscle force transmission
Invoked in Methods, Model Creation, where the LoA algorithm replaces volumetric estimation; the paper provides no validation that centroid paths reproduce muscle moment arms or forces.
domain assumption The dm_control skeleton and 3D muscle meshes are anatomically accurate enough for validation
The model is built on existing leo3Dmodels assets in dm_control; no independent anatomical verification is provided.
domain assumption Qualitative comparison with EMG from different breeds and older studies is a valid validation
The EMG data are from literature across various breeds, while the model is a Pharaoh Dog and mocap is a Border Collie; the paper acknowledges this as a limitation but still uses it as the main validation.
ad hoc to paper Sigmoid-smoothed activation/deactivation dynamics preserve the biomechanical behavior of Millard's model
The proposed tau_smooth modification in Eq. (5) is introduced to aid differentiability; no experiment shows it reproduces Millard dynamics outside the tracking task.

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Cite this review

Pith. "Pith review of Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model." pith.science (2026). https://pith.science/paper/SJHHP44N

@misc{pith2026250623768,
  author       = {Pith},
  title        = {Pith review of: Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SJHHP44N}},
  note         = {Machine review of arXiv:2506.23768}
}

read the original abstract

We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Alizadeh, G

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