Humanoid Robot Imitation with Pose Similarity Metric Learning

Abstract

Imitation is considered to be a kind of social learning that allows the transfer of information, actions, behaviours, etc. Whereas current robots are unable to perform as many tasks as human, it is a natural way for them to learn by imitations, just as human does. With the humanoid robots being more intelligent, the field of robot imitation has getting noticeable advance. In this paper, we focus on the pose imitation between a human and a humanoid robot and learning a similarity metric between human pose and robot pose. In contrast to recent approaches that capture human data using expensive motion captures or only imitate the upper body movements, our framework adopts a Kinect instead and can deal with complex, whole body motions by keeping both single pose balance and pose sequence balance. Meanwhile, different from previous work that employs subjective evaluation, we propose a pose similarity metric based on the shared structure of the motion spaces of human and robot. The qualitative and quantitative experimental results demonstrate a satisfactory imitation performance and indicate that the proposed pose similarity metric is discriminative.