A methodological framework for robotic reproduction of observed human actions: Formulation using latent space representation

The current work presents a comprehensive methodological framework that facilitates robots to acquire human-like behavioral acts by observing human demonstrators. Accordingly, the introduced framework is established as a Learning from Demonstration (LfD) process that enables the reproduction of either learned or novel actions. Mapping of human actions to the respective robotic ones is achieved via an indeterminate depiction, termed latent space representation. The latter accomplishes a compact, yet precise abstraction of action trajectories, effectively representing high dimensional raw actions in a low dimensional space. Extensive experimentation with a real robotic arm demonstrates the robustness and applicability of the introduced framework.