Bibliography

  1. Geijtenbeek, T., van der Stappen, A. F., & Panne, M. (2013). Flexible Muscle-Based Locomotion for Bipedal Creatures. ACM Transactions on Graphics, 32(6), 206:1–206:11. https://doi.org/10.1145/2508363.2508399.
  2. Hill, A.W. (1938). The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London Series B, 126(843), 136–195.
  3. Zhou, J., Chen, J., Deng, H., & Qiao, H. (2019). From rough to precise: human-inspired phased target learning framework for redundant musculoskeletal systems. Frontiers in Neurorobotics, 13. https://doi.org/10.3389/fnbot.2019.00061

Further Reading

If you’re looking to understand the implementation of Hill-type muscle models in a general way (Zajac, 1989) is a good paper to understand. (Günther, et al., 2012) is an interesting treatment of adding mass to Hill models. The rest of these are resources for approaches to muscle-based motor control.

  1. Dongsung H. & Todorov, E. (2009). Real-time motor control using recurrent neural networks. 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). Nashville, TN, USA. https://doi.org/10.1109/ADPRL.2009.4927524
  2. Driess, D., Zimmermann, H., Wolfen, S., Suissa, D., Haeufle, D., Hennes, D., Toussaint, M., & Schmitt, S. (2018). Learning to control redundant musculoskeletal systems with neural networks and SQP: exploiting muscle properties. 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD, Australia. https://doi.org/10.1109/icra.2018.8463160
  3. Günther M., Röhrle O., Haeufle D. F., & Schmitt S. (2012). Spreading out muscle mass within a Hill-type model: a computer simulation study. Computational and Mathematical Methods in Medicine, 2012. https://doi.org/10.1155/2012/848630
  4. Rückert, E.A., & d’Avella, A. (2013). Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers in Computational Neuroscience, 7. https://doi.org/10.3389/fncom.2013.00138
  5. Wochner, I., Driess, D., Zimmermann, H., Haeufle, D. F. B., Toussaint, M., & Schmitt, S. (2020). Optimality principles in human point-to-manifold reaching accounting for muscle dynamics. Frontiers in Computational Neuroscience, 14. https://doi.org/10.3389/fncom.2020.00038
  6. Zajac F. E. (1989). Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Critical reviews in biomedical engineering, 17(4), 359–411.