Accelerating humanoid robot learning from human action skills using context-aware middleware

Abstract

In this paper we propose the creation of context-aware middleware to solve the challenge of integrating disparate incompatible systems involved in the teaching of human action skills to robots. Context-aware middleware provides the solution to retrofitting capabilities onto existing robots (agents) and bridges the technology differences between systems. The experimental results demonstrate a framework for handling situational and contextual data for robot Learning from Demonstration.