Abstract
Muscle-actuated control is a research topic of interest spanning different fields, in particular biomechanics, robotics and graphics. This type of control is particularly challenging because models are often overactuated, and dynamics are delayed and non-linear. It is however a very well tested and tuned actuation model that has undergone millions of years of evolution and that involves interesting properties exploiting passive forces of muscle-tendon units and efficient energy storage and release. To facilitate research on muscle-actuated simulation, we release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo simulator. Ostriches are one of the fastest bipeds on earth and are therefore an excellent model for studying muscle-actuated bipedal locomotion. The model is based on CT scans and dissections used to gather actual muscle data such as insertion sites, lengths and pennation angles. Along with this model, we also provide a set of reinforcement learning tasks, including reference motion tracking and a reaching task with the neck. The reference motion data are based on motion capture clips of various behaviors which we pre-processed and adapted to our model. This paper describes how the model was built and iteratively improved using the tasks. We evaluate the accuracy of the muscle actuation patterns by comparing them to experimentally collected electromyographic data from locomoting birds. We believe that this work can be a useful bridge between the biomechanics, reinforcement learning, graphics and robotics communities, by providing a fast and easy to use simulation.
Abstract (translated)
URL
https://arxiv.org/abs/2112.06061
