A deep learning framework for realistic robot motion generation

Humanoid robots are being developed to play the role of personal assistants. With the development of artificial intelligence technology, humanoid robots are expected to perform many human tasks, such as housework, human care, and even medical treatment. However, robots cannot currently move flexibly like humans, which affects their fine motor skill performance. This is primarily because traditional robot control methods use manipulators that are difficult to articulate well. To solve this problem, we propose a nonlinear realistic robot motion generation method based on deep learning. Our method benefits from decomposing human motions into basic motions and realistic motions using the multivariate empirical mode decomposition and learning the biomechanical relationships between them by using an autoencoder generation network. The experimental results show that realistic motion features can be learned by the generation network and motion realism can be increased by adding the learned motions to the robots.