Connecting Natural Language to Task Demonstrations and Low-level Control of Industrial Robots

Industrial robotics is a complex domain, not easily amenable to formalization using semantic technologies. It involves such disparate aspects of the real world as geometry, dynamics, constraint-satisfaction, planning and scheduling, real-time control, robot-robot and human-robot communication and, finally, intentions of the robot user. To represent so different kinds of knowledge is a challenge and the research on combining those topics is only in its infancy. This paper describes our attempts to combine descriptions of robot tasks using natural language together with their realizations using robot hardware involving force sensing, ultimately leading to a potential of learning new robot skills employing force-based assembly. We believe it is a novel approach opening possibilities of semantic anchoring for learning from demonstration.