This link describes our experience implementing a system which learns locomotion skills for humanoid skeletons from imitation, and all of the supporting infrastructure and data processing necessary to do so. Our system employs a generative adversarial imitation learning (GAIL) architecture, which is a type of generative adversarial network. We successfully trained our GAIL to control a custom-designed humanoid skeleton, using expert demonstrations from a reinforcment-learned (RL) policy using that skeleton. We also explored several methods for deriving real human motion demonstrations from video and developed a preprocessing pipeline for motion capture data. Our system is work-in-progress, making it the foundation for several possible future research projects.
The complete post can be found here.