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ARXIV:2603.25544 · ROBOTICS · SUBMITTED 27 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.25544ROBOTICSSUBMITTED 27 MAR · 20:30 UTCFRESHNESS STALEChengkun Li · Cheryl Wang · Bianca Ziliotto · Merkourios Simos · Jozsef Kovecses · Guillaume Durandau · +1 at arXiv
A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements.
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Pain A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements.
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A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements.
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10.48550/arXiv.2603.25544A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements.
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
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PROBLEM
A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realis...
METHOD
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...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 sin...
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated humanoids.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for scalable, physiologically realistic full-body musculoskeletal motor learning, enabling rapid training and fine-tuning of human-like movements.
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