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.