References

Papers, datasets and software that MyoSuite builds on, grouped by topic. The page Publications lists the MyoSuite papers; this page also covers the sources behind the models, the muscle conditions, the controllers and the motion data. If you use one of these parts of MyoSuite, please cite the corresponding work.

MyoSuite and the MuscleMimic integration

  • Caggiano, V., Wang, H., Durandau, G., Sartori, M., Kumar, V. (2022). MyoSuite – A contact-rich simulation suite for musculoskeletal motor control. Learning for Dynamics and Control (L4DC). https://arxiv.org/abs/2205.13600

  • Wang, H., Caggiano, V., Durandau, G., Sartori, M., Kumar, V. (2022). MyoSim: Fast and physiologically realistic MuJoCo models for musculoskeletal and exoskeletal studies. IEEE International Conference on Robotics and Automation (ICRA). https://ieeexplore.ieee.org/abstract/document/9811684

  • Caggiano, V., Dasari, S., Kumar, V. (2023). MyoDex: A generalizable prior for dexterous manipulation. International Conference on Machine Learning (ICML). https://arxiv.org/abs/2309.03130

  • Berg, C., Caggiano, V., Kumar, V. (2023). SAR: Generalization of physiological dexterity via synergistic action representation. Robotics: Science and Systems (RSS). https://arxiv.org/abs/2307.03716

  • Caggiano, V. et al. (2023). MyoChallenge 2022: Learning contact-rich manipulation using a musculoskeletal hand. Proceedings of the NeurIPS 2022 Competitions Track, PMLR 220, 233-250. https://proceedings.mlr.press/v220/caggiano23a.html

  • Li, C., Wang, C., Ziliotto, B., Simos, M., Kovecses, J., Durandau, G., Mathis, A. (2026). Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale. arXiv:2603.25544. https://arxiv.org/abs/2603.25544

Work built on MyoSuite

  • Schumacher, P., Haeufle, D.F.B., Büchler, D., Schmitt, S., Martius, G. (2023). DEP-RL: Embodied exploration for reinforcement learning in overactuated and musculoskeletal systems. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=C-xa_D3oTj6

  • Chiappa, A.S., Marin Vargas, A., Huang, A.Z., Mathis, A. (2023). Latent exploration for reinforcement learning. Advances in Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2305.20065

  • Hodossy, B.K., Crotti, M., Pace, A., Catalano, M.G., Aszmann, O.C., Bicchi, A., Farina, D. (2026). Towards a Virtual Gait Lab: Testing Prosthetics with Dynamically Simulated Users. IEEE Transactions on Medical Robotics and Bionics. https://ieeexplore.ieee.org/abstract/document/11606461

  • Bhattarai, A., Selder, H., Fischer, F., Fleig, A., Kristensson, P.O. (2026). MyoInteract: A framework for fast prototyping of biomechanical HCI tasks using reinforcement learning. ACM Designing Interactive Systems Conference (DIS). https://arxiv.org/abs/2602.15245, https://doi.org/10.1145/3800645.3812899 (builds on MyoSuite; trains and evaluates muscle-actuated simulated users from a GUI)

Motion data and retargeting (MuscleMimic, tutorials 5.1-5.5)

The MuscleMimic motions are derived from the AMASS archive, whose KIT subset provides the locomotion clips (for example KIT/314/walking_medium09_poses), and retargeted to the MyoFullBody and MyoBimanualArm models with GMR. The retargeted datasets are non-commercial research data under the AMASS license; the dataset cards ask you to cite AMASS and the MuscleMimic paper.

  • Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J. (2019). AMASS: Archive of motion capture as surface shapes. International Conference on Computer Vision (ICCV), 5442-5451. https://arxiv.org/abs/1904.03278 (license and data: https://amass.is.tue.mpg.de)

  • Mandery, C., Terlemez, Ö., Do, M., Vahrenkamp, N., Asfour, T. (2015). The KIT whole-body human motion database. International Conference on Advanced Robotics (ICAR), 329-336.

  • Mandery, C., Terlemez, Ö., Do, M., Vahrenkamp, N., Asfour, T. (2016). Unifying representations and large-scale whole-body motion databases for studying human motion. IEEE Transactions on Robotics 32(4), 796-809. https://doi.org/10.1109/TRO.2016.2572685 (AMASS asks to cite one of the two KIT papers when KIT motions are used; both are listed here.)

  • Araújo, J.P., Ze, Y., Xu, P., Wu, J., Liu, C.K. (2025). Retargeting matters: General motion retargeting for humanoid motion tracking (GMR). arXiv:2510.02252. https://arxiv.org/abs/2510.02252 (MuscleMimic uses the fork https://github.com/amathislab/gmr_plus)

  • Simos, M., Chiappa, A.S., Mathis, A. (2025). KINESIS: Motion imitation for human musculoskeletal locomotion. arXiv:2503.14637. https://arxiv.org/abs/2503.14637 (source of the KIT training and testing splits KIT_KINESIS_TRAINING_MOTIONS / KIT_KINESIS_TESTING_MOTIONS)

  • ULTRA-MoCap v1 upper-limb dataset (Fritsche, O. et al.), figshare, https://doi.org/10.6084/m9.figshare.28751156.v1, CC BY 4.0. Source of the 208 ULTRA-MoCap trajectories of the bimanual retargeted dataset.

Models and biomechanics

  • Delp, S.L. et al. (2007). OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering 54(11), 1940-1950. https://doi.org/10.1109/TBME.2007.901024

  • Seth, A. et al. (2018). OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Computational Biology 14(7), e1006223. https://doi.org/10.1371/journal.pcbi.1006223

  • Rajagopal, A. et al. (2016). Full-body musculoskeletal model for muscle-driven simulation of human gait. IEEE Transactions on Biomedical Engineering 63(10), 2068-2079. https://ieeexplore.ieee.org/document/7505900

  • Xu, Z., Kumar, V., Matsuoka, Y., Todorov, E. (2012). Design of an anthropomorphic robotic finger system with biomimetic artificial joints. IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). https://doi.org/10.1109/BioRob.2012.6290710

  • Thelen, D.G., Anderson, F.C., Delp, S.L. (2003). Generating dynamic simulations of movement using computed muscle control. Journal of Biomechanics 36(3), 321-328. https://doi.org/10.1016/S0021-9290(02)00432-3 (tutorial 3.4)

  • Todorov, E., Erez, T., Tassa, Y. (2012). MuJoCo: A physics engine for model-based control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5026-5033. https://doi.org/10.1109/IROS.2012.6386109

Muscle fatigue (myoFati*, tutorial 4.2)

The model and its parameters are validated on the page Fatigue Model Validation, which also lists the endurance-time sources.

  • Xia, T., Frey-Law, L.A. (2008). A theoretical approach for modeling peripheral muscle fatigue and recovery. Journal of Biomechanics 41(14), 3046-3052. https://doi.org/10.1016/j.jbiomech.2008.07.013

  • Frey-Law, L.A., Avin, K.G. (2010). Endurance time is joint-specific: A modelling and meta-analysis investigation. Ergonomics 53(1), 109-129. https://doi.org/10.1080/00140130903389068

  • Frey-Law, L.A., Looft, J.M., Heitsman, J. (2012). A three-compartment muscle fatigue model accurately predicts joint-specific maximum endurance times for sustained isometric tasks. Journal of Biomechanics 45(10), 1803-1808. https://doi.org/10.1016/j.jbiomech.2012.04.018

  • Looft, J.M., Herkert, N., Frey-Law, L. (2018). Modification of a three-compartment muscle fatigue model to predict peak torque decline during intermittent tasks. Journal of Biomechanics 77, 16-25. https://doi.org/10.1016/j.jbiomech.2018.06.005

  • Looft, J.M., Frey-Law, L.A. (2020). Adapting a fatigue model for shoulder flexion fatigue: Enhancing recovery rate during intermittent rest intervals. Journal of Biomechanics 106, 109762. https://doi.org/10.1016/j.jbiomech.2020.109762

  • Rakshit, R., Xiang, Y., Yang, J. (2021). Functional muscle group- and sex-specific parameters for a three-compartment controller muscle fatigue model applied to isometric contractions. Journal of Biomechanics 127, 110695. https://doi.org/10.1016/j.jbiomech.2021.110695

  • Cheema, N., Frey-Law, L.A., Naderi, K., Lehtinen, J., Slusallek, P., Hämäläinen, P. (2020). Predicting mid-air interaction movements and fatigue using deep reinforcement learning. ACM CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3313831.3376701 (implementation of the fatigue model in the User-in-the-Box framework, which the CPU model follows)

  • Daly, M., Vidt, M.E., Eggebeen, J.D., Simpson, W.G., Miller, M.E., Marsh, A.P., Saul, K.R. (2013). Upper extremity muscle volumes and functional strength after resistance training in older adults. Journal of Aging and Physical Activity 21(2), 186-207. https://doi.org/10.1123/japa.21.2.186 (functional muscle groups of the shoulder, wrist and elbow)

Motor control, locomotion and synergies

  • Harris, C.M., Wolpert, D.M. (1998). Signal-dependent noise determines motor planning. Nature 394, 780-784. https://doi.org/10.1038/29528 (signal-dependent motor noise)

  • van Beers, R.J., Haggard, P., Wolpert, D.M. (2004). The role of execution noise in movement variability. Journal of Neurophysiology 91(2), 1050-1063. https://doi.org/10.1152/jn.00652.2003 (default motor-noise levels, MotorNoiseCfg.van_beers_2004())

  • Song, S., Geyer, H. (2015). A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion. The Journal of Physiology 593(16), 3493-3511. https://doi.org/10.1113/JP270228 (reflex walking controller of tutorial 2.4)

  • Tresch, M.C., Cheung, V.C.K., d’Avella, A. (2006). Matrix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and experimental data sets. Journal of Neurophysiology 95(4), 2199-2212. https://doi.org/10.1152/jn.00222.2005 (tutorial 2.3)

Reinforcement learning and numerics

  • Peng, X.B., Abbeel, P., Levine, S., van de Panne, M. (2018). DeepMimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions on Graphics 37(4), 143. https://doi.org/10.1145/3197517.3201311 (multi-term tracking reward and reference state initialization of the Mimic envs)

  • Pardo, F., Tavakoli, A., Levdik, V., Kormushev, P. (2018). Time limits in reinforcement learning. International Conference on Machine Learning (ICML). https://arxiv.org/abs/1712.00378 (truncation handling in tutorials/files/5.2/train_mimic.py)

  • Fischer, F., Bachinski, M., Klar, M., Fleig, A., Müller, J. (2021). Reinforcement learning control of a biomechanical model of the upper extremity. Scientific Reports 11, 14445. https://doi.org/10.1038/s41598-021-93760-1 (motor-noise levels 0.103 and 0.185)

  • Ikkala, A., Fischer, F., Klar, M., Bachinski, M., Fleig, A., Howes, A., Hämäläinen, P., Müller, J., Murray-Smith, R., Oulasvirta, A. (2022). Breathing life into biomechanical user models. ACM Symposium on User Interface Software and Technology (UIST). https://doi.org/10.1145/3526113.3545689 (User-in-the-Box; motor noise on the controls)

  • Timmer, J., Koenig, M. (1995). On generating power law noise. Astronomy & Astrophysics 300, 707-710. (colored exploration noise)

  • Buss, S.R. (2004). Introduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methods. Technical note (myosuite.physics.inverse_kinematics).