We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.

翻译：我们提出了一种新颖的犬类肌肉骨骼模型，该模型通过精确的三维肌肉网格程序化生成。与此模型配套的是一种基于动作捕捉的运动任务，兼容多种控制算法，以及一种改进的肌肉动力学模型，旨在提升可微控制框架中的收敛性能。通过将模拟的肌肉激活模式与先前犬类运动实验获得的肌电图数据进行比较，我们验证了该方法。本研究旨在弥合生物力学、机器人学和计算神经科学之间的鸿沟，为研究肌肉驱动和神经肌肉控制的研究人员提供一个稳健平台。我们计划发布完整模型及重新定向的动作捕捉片段，以促进进一步的研究与开发。