Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale

TL;DR

MuscleMimic is an open-source framework that enables scalable, physiologically realistic motion imitation learning for full-body musculoskeletal humanoids, significantly accelerating training and facilitating research in neuromuscular control.

Contribution

The paper introduces MuscleMimic, a scalable, GPU-accelerated platform with validated musculoskeletal models and a retargeting pipeline, advancing full-body motor learning at scale.

Findings

Order-of-magnitude faster training with GPU simulation

High fidelity reproduction of human movements

Strong biomechanical validation against experimental data

Abstract

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