Robotic Imitation of Human Actions
Josua Spisak Matthias Kerzel Stefan Wermter
Imitation can allow us to quickly gain an understanding of a new task.
Through a demonstration, we can gain direct knowledge about which actions need
to be performed and which goals they have. In this paper, we introduce a new
approach to imitation learning that tackles the challenges of a robot imitating
a human, such as the change in perspective and body schema. Our approach can
use a single human demonstration to abstract information about the demonstrated
task, and use that information to generalise and replicate it. We facilitate
this ability by a new integration of two state-of-the-art methods: a diffusion
action segmentation model to abstract temporal information from the
demonstration and an open vocabulary object detector for spatial information.
Furthermore, we refine the abstracted information and use symbolic reasoning to
create an action plan utilising inverse kinematics, to allow the robot to
imitate the demonstrated action.