  Teleoperation enables a user to perform dangerous tasks (e.g., work in
disaster zones or in chemical plants) from a remote location. Nevertheless,
common approaches often provide cumbersome and unnatural usage. In this letter,
we propose TeleFMG, an approach for teleoperation of a multi-finger robotic
hand through natural motions of the user's hand. By using a low-cost wearable
Force-Myography (FMG) device, musculoskeletal activities on the user's forearm
are mapped to hand poses which, in turn, are mimicked by a robotic hand. The
mapping is performed by a spatio-temporal data-based model based on the
Temporal Convolutional Network. The model considers spatial positions of the
sensors on the forearm along with temporal dependencies of the FMG signals. A
set of experiments show the ability of a teleoperator to control a multi-finger
hand through intuitive and natural finger motion. A robot is shown to
successfully mimic the user's hand in object grasping and gestures.
Furthermore, transfer to a new user is evaluated while showing that fine-tuning
with a limited amount of new data significantly improves accuracy.
