  Collaborative robots are expected to be able to work alongside humans and in
some cases directly replace existing human workers, thus effectively responding
to rapid assembly line changes. Current methods for programming contact-rich
tasks, especially in heavily constrained space, tend to be fairly inefficient.
Therefore, faster and more intuitive approaches to robot teaching are urgently
required. This work focuses on combining visual servoing based learning from
demonstration (LfD) and force-based learning by exploration (LbE), to enable
fast and intuitive programming of contact-rich tasks with minimal user effort
required. Two learning approaches were developed and integrated into a
framework, and one relying on human to robot motion mapping (the visual
servoing approach) and one on force-based reinforcement learning. The developed
framework implements the non-contact demonstration teaching method based on
visual servoing approach and optimizes the demonstrated robot target positions
according to the detected contact state. The framework has been compared with
two most commonly used baseline techniques, pendant-based teaching and
hand-guiding teaching. The efficiency and reliability of the framework have
been validated through comparison experiments involving the teaching and
execution of contact-rich tasks. The framework proposed in this paper has
performed the best in terms of teaching time, execution success rate, risk of
damage, and ease of use.
