OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
This work provides a simulation tool for studying muscle-actuated bipedal locomotion, bridging biomechanics, neuroscience, and robotics, but it is incremental as it builds on existing methods with new data.
The researchers tackled the challenge of muscle-actuated control by developing a 3D musculoskeletal simulation of an ostrich using MuJoCo, based on CT scans and dissections, and evaluated it with reinforcement learning tasks and electromyographic data, showing that rich reward signals or regularization are needed for realistic movements.
Muscle-actuated control is a research topic that spans multiple domains, including biomechanics, neuroscience, reinforcement learning, robotics, and graphics. This type of control is particularly challenging as bodies are often overactuated and dynamics are delayed and non-linear. It is however a very well tested and tuned actuation mechanism that has undergone millions of years of evolution with interesting properties exploiting passive forces and efficient energy storage of muscle-tendon units. To facilitate research on muscle-actuated simulation, we release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo physics engine. The ostrich is one of the fastest bipeds on earth and therefore makes an excellent model for studying muscle-actuated bipedal locomotion. The model is based on CT scans and dissections used to collect 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, running, and neck control, used to infer muscle actuation patterns. The reference motion data is based on motion capture clips of various behaviors that we preprocessed and adapted to our model. This paper describes how the model was built and iteratively improved using the tasks. We also evaluate the accuracy of the muscle actuation patterns by comparing them to experimentally collected electromyographic data from locomoting birds. The results demonstrate the need for rich reward signals or regularization techniques to constrain muscle excitations and produce realistic movements. Overall, we believe that this work can provide a useful bridge between fields of research interested in muscle actuation.