In this work, we conduct a systematic comparison of two state-of-the-art motion-imitation reinforcement learning (MIRL) pipelines, one built on SCONE/HyFyDy and one built on MuJoCo/MyoSim. HyFyDy emphasizes physiological realism through detailed musculotendon modeling, while MuJoCo prioritizes computational efficiency and scalable policy learning. While recent work has demonstrated that both pipelines reproduce human kinematics with high fidelity, it remains unclear if they accurately capture the underlying neuromuscular behavior that produced the movement. This limitation is particularly important for robotic assistive-device design and control, where outcome measures such as muscle activation patterns and metabolic cost are often used as optimization targets. To conduct a systematic comparison, our work compares both pipelines using a common set of human motion-capture and electromyography (EMG) measurements. The results find that while both pipelines produce similar kinematics with relative accuracy, the muscle activations from HyFyDy are more aligned with the experimental EMG, as supported by the average pooled (RMSE, r) values for muscle activations from HyFyDy and MuJoCo: (0.164, 0.4) and (0.344, 0.11), respectively. While we conclude that the more advanced physiological realism of HyFyDy currently makes it more suitable for musculoskeletal modeling, both require further development to bring physiological realism to GPU-parallelizable simulation environments and advance robotic assistive device design.
Figures & tables
Fig. 1: This work benchmarks the predictive capabilities of two state-of-the-art musculoskeletal modeling simulation environments intended for motion-imitation-style reinforcement learning: HyFyDy/SCONE (middle) and MyoSim/MuJoCo (bottom), against real-world human data modeled in OpenSim (top). The gait tiles show each musculoskeletal model completing one gait cycle with the average speed-binned muscle activations for the medial gastrocnemius throughout it. Darker and lighter colors represent slower and faster speeds, respectively.
Fig. 2: Pipelines for MuscleMimic and HyFyDy-IL with modifications for this comparative study. The first models correspond to the comparison between the frameworks with similar standard models; the second model is the scaled HyFyDy model for the second comparison; and the third models are the 3D models for the final comparison.
Fig. 3: Two-dimensional models MM240-2D (left), H1090 (middle), and H-AB08 (right). Not shown: MM240-3D and H2190 (visually identical to MM240-2D and H1090 ).
Term k
Tracked quantity
βk
wk
pos
Kinematic pose error
1.0
0.60
vel
Kinematic velocity error
1.0
0.15
root
End-effector position error
80.0
0.25
eff
Muscle activation magnitude
1.0
−2.00
Δeff
Muscle excitation rate
1.0
−2.00
Total ( ∑k∣wk∣ )
5.00
TABLE I: Original coefficients for HyFyDy-IL (top) and MuscleMimic (bottom) reward functions.
H1090
H-AB08
H2190
MM240-2D
MM240-3D 1 1 1 MM240-3D was excluded from statistical computations due to its inability to complete the 140-second walking motion, instead only completing approximately 18-seconds.
Fig. 4: Hip, knee, and ankle angle data from the human data (top) from the policies’ rollouts over identical 140-second walking data, normalized over a gait cycle. Models H1090 (second row), H-AB08 (third row), and H2190 (bottom row), MM240-2D (fourth row), MM240-3D (fifth row).
Fig. 5: The top row shows EMG experimental data recorded from subject AB08, which serves as the ground-truth benchmark for comparison. The rows below show muscle activations, normalized over a gait cycle, which are produced by each policy’s rollouts on subject AB08’s walking data. Eight muscles crucial for walking were analyzed: tibialis anterior, rectus femoris, biceps femoris, gluteus medius, medial gastrocnemius, vastus lateralis, adductor longus, and gluteus maximus. Models H1090 (second row), H-AB08 (third row), and H2190 (fourth row), MM240-2D (fifth row), MM240-3D (sixth row). Colors indicate the speeds the models were able to achieve. Note that none of the policies were able to reach the peak speed of 2.5 m/s. Also note that MM240-2D learned not to activate several muscles: the gluteus medius, vastus lateralis, and gluteus maximus, which are necessary for walking.
Learning motor control for muscle-driven musculoskeletal models is hindered by the computational cost of biomechanically accurate simulation and the scarcity of validated, open full-body models. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated humanoids. MuscleMimic provides two validated musculoskeletal embodiments - a fixed-root upper-body model (126 muscles) for bimanual manipulation and a full-body model (416 muscles) for locomotion - together with a retargeting pipeline that maps SMPL-format motion capture data onto musculoskeletal structures while preserving kinematic and dynamic consistency. Leveraging massively parallel GPU simulation, the framework achieves order-of-magnitude training speedups over prior CPU-based approaches while maintaining comprehensive collision handling, enabling a single generalist policy to be trained on hundreds of diverse motions within days. The resulting policy faithfully reproduces a broad repertoire of human movements under full muscular control and can be fine-tuned to novel motions within hours. Biomechanical validation against experimental walking and running data demonstrates strong agreement in joint kinematics (mean correlation r = 0.90), while muscle activation analysis reveals both the promise and fundamental challenges of achieving physiological fidelity through kinematic imitation alone. By lowering the computational and data barriers to musculoskeletal simulation, MuscleMimic enables systematic model validation across diverse dynamic movements and broader participation in neuromuscular control research. Code, models, checkpoints, and retargeted datasets are available at: https://github.com/amathislab/musclemimic
Chengkun Li, Cheryl Wang, Bianca Ziliotto +4
EPFL, Switzerland · McGill University, Canada · Feil & Oberfeld CRIR-Jewish Rehabilitation Hospital Research Center, Canada +1
The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the high-dimensional, over-actuated system. Forward imitation approaches based on deep reinforcement learning exhibited inadequate tracking performance due to the curse of dimensionality in both control and reward design. Here we introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction. By integrating large-scale parallel GPU simulation with adversarial reward aggregation and value-guided flow exploration, the MS-Emulator framework overcomes key optimization bottlenecks in high-dimensional reinforcement learning for musculoskeletal control, which accurately reproduces a broad repertoire of motions in a whole-body human musculoskeletal system actuated by approximately 700 muscles. It achieved high joint angle accuracy and body position alignment for highly dynamic tasks such as dance, cartwheel, and backflip. The framework was also used to explore the musculoskeletal control solution space, identifying distinct musculoskeletal control policies that converge to nearly identical external kinematic and mechanical measurements. This work establishes a tractable computational route to analyzing the specificity and diversity underlying human embodied control of movement. Project page: https://lnsgroup.cc/research/MS-Emulator.
Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging. We present a physiology-informed reinforcement-learning framework that constrains control using muscle synergies. We extracted a low-dimensional synergy basis from inverse musculoskeletal analyses of a small set of overground walking trials and used it as the action space for a muscle-driven three-dimensional model trained across variable speeds, slopes and uneven terrain. The resulting controller generated stable gait from 0.7-1.8 m/s and on ± 6∘ grades and reproduced condition-dependent modulation of joint angles, joint moments and ground reaction forces. Compared with an unconstrained controller, synergy-constrained control reduced non-physiological knee kinematics and kept knee moment profiles within the experimental envelope. Across conditions, simulated vertical ground reaction forces correlated strongly with human measurements, and muscle-activation timing largely fell within inter-subject variability. These results show that embedding neurophysiological structure into reinforcement learning can improve biomechanical fidelity and generalization in predictive human locomotion simulation with limited experimental data.
Ilseung Park, Eunsik Choi, Jangwhan Ahn +1
Carnegie Mellon University · Department of Physical Education, Seoul National University, Seoul, Republic of Korea · Seoul National University +2