  In this paper, we present a multimodal mobile teleoperation system that
consists of a novel vision-based hand pose regression network (Transteleop) and
an IMU-based arm tracking method. Transteleop observes the human hand through a
low-cost depth camera and generates not only joint angles but also depth images
of paired robot hand poses through an image-to-image translation process. A
keypoint-based reconstruction loss explores the resemblance in appearance and
anatomy between human and robotic hands and enriches the local features of
reconstructed images. A wearable camera holder enables simultaneous hand-arm
control and facilitates the mobility of the whole teleoperation system. Network
evaluation results on a test dataset and a variety of complex manipulation
tasks that go beyond simple pick-and-place operations show the efficiency and
stability of our multimodal teleoperation system.
