Gait Generation for Legged Robot by Encoder-Decoder Model

Abstract

In the future, it is expected that robots will collaborate with humans. Such robots for collaborative works need to perform a variety of movements. Therefore, it is necessary to develop a system that can efficiently generate various movements from limited training datasets. To develop such a system, we focus on latent variables that represent features of the movements. This study proposes an encoder-decoder model, a kind of neural network, that generates a walking motion for a legged robot from the center-of-mass trajectory through latent variables. By directly changing the latent variable, we were able to let the legged robot walk at a different velocity. The experimental results suggest that the proposed system can generate walking motions with various velocities from learning a limited variety of movements.