  This study proposes an approach to human-to-humanoid teleoperation using
GAN-based online motion retargeting, which obviates the need for the
construction of pairwise datasets to identify the relationship between the
human and the humanoid kinematics. Consequently, it can be anticipated that our
proposed teleoperation system will reduce the complexity and setup requirements
typically associated with humanoid controllers, thereby facilitating the
development of more accessible and intuitive teleoperation systems for users
without robotics knowledge. The experiments demonstrated the efficacy of the
proposed method in retargeting a range of upper-body human motions to humanoid,
including a body jab motion and a basketball shoot motion. Moreover, the
human-in-the-loop teleoperation performance was evaluated by measuring the
end-effector position errors between the human and the retargeted humanoid
motions. The results demonstrated that the error was comparable to those of
conventional motion retargeting methods that require pairwise motion datasets.
Finally, a box pick-and-place task was conducted to demonstrate the usability
of the developed humanoid teleoperation system.
