A novel teacher-student hierarchical approach for learning primitive information

In this study, we focus on the challenge of robot motion generation, which is critical for improving the ability of robots to perform downstream tasks. To this end, inspired by the idea of variational inference, we propose a novel teacher-student hierarchical approach for learning continuous primitive spaces to generate robot motions. We first learn a variational auto-encoder (VAE) based on Gated Recurrent Unit (GRU) from demonstrations, the encoder is used as a teacher model to encode trajectories to primitive information, and the decoder aims to decode latent information to joint trajectories. To learn a primitive action space for the high-level model, we propose a novel learning method to train a student model. We utilize the trained teacher to encode trajectories to labels, and use the Kullback-Leibler (KL) divergence and the reconstruction errors between the label and the predicted primitive information as the loss to train a student model. We then utilize trained student models to guide high-level policy learning. We evaluate our approach on three different robot datasets. Experiments demonstrate that our method obtains the ability to generate new motion and discover common information across tasks, it also can be used to accelerate downstream task learning.