  Employing a teleoperation system for gathering demonstrations offers the
potential for more efficient learning of robot manipulation. However,
teleoperating a robot arm equipped with a dexterous hand or gripper, via a
teleoperation system poses significant challenges due to its high
dimensionality, complex motions, and differences in physiological structure.
  In this study, we introduce a novel system for joint learning between human
operators and robots, that enables human operators to share control of a robot
end-effector with a learned assistive agent, facilitating simultaneous human
demonstration collection and robot manipulation teaching. In this setup, as
data accumulates, the assistive agent gradually learns. Consequently, less
human effort and attention are required, enhancing the efficiency of the data
collection process. It also allows the human operator to adjust the control
ratio to achieve a trade-off between manual and automated control.
  We conducted experiments in both simulated environments and physical
real-world settings. Through user studies and quantitative evaluations, it is
evident that the proposed system could enhance data collection efficiency and
reduce the need for human adaptation while ensuring the collected data is of
sufficient quality for downstream tasks. Videos are available at
https://norweig1an.github.io/human-agent-joint-learning.github.io/.
