  This paper proposes a vision-based framework for a 7-degree-of-freedom
robotic manipulator, with the primary objective of facilitating its capacity to
acquire information from human hand demonstrations for the execution of
dexterous pick-and-place tasks. Most existing works only focus on the position
demonstration without considering the orientations. In this paper, by employing
a single depth camera, MediaPipe is applied to generate the three-dimensional
coordinates of a human hand, thereby comprehensively recording the hand's
motion, encompassing the trajectory of the wrist, orientation of the hand, and
the grasp motion. A mean filter is applied during data pre-processing to smooth
the raw data. The demonstration is designed to pick up an object at a specific
angle, navigate around obstacles in its path and subsequently, deposit it
within a sloped container. The robotic system demonstrates its learning
capabilities, facilitated by the implementation of Dynamic Movement Primitives,
enabling the assimilation of user actions into its trajectories with different
start and end poi
