  In this paper, we present a complete and efficient implementation of a
knowledge-sharing augmented kinesthetic teaching approach for efficient task
execution in robotics. Our augmented kinesthetic teaching method integrates
intuitive human feedback, including verbal, gesture, gaze, and physical
guidance, to facilitate the extraction of multiple layers of task information
including control type, attention direction, input and output type, action
state change trigger, etc., enhancing the adaptability and autonomy of robots
during task execution. We propose an efficient Programming by Demonstration
(PbD) framework for users with limited technical experience to teach the robot
in an intuitive manner. The proposed framework provides an interface for such
users to teach customized tasks using high-level commands, with the goal of
achieving a smoother teaching experience and task execution. This is
demonstrated with the sample task of pouring water.
