  We present a method for EMG-driven teleoperation of non-anthropomorphic robot
hands. EMG sensors are appealing as a wearable, inexpensive, and unobtrusive
way to gather information about the teleoperator's hand pose. However, mapping
from EMG signals to the pose space of a non-anthropomorphic hand presents
multiple challenges. We present a method that first projects from forearm EMG
into a subspace relevant to teleoperation. To increase robustness, we use a
model which combines continuous and discrete predictors along different
dimensions of this subspace. We then project from the teleoperation subspace
into the pose space of the robot hand. Our method is effective and intuitive,
as it enables novice users to teleoperate pick and place tasks faster and more
robustly than state-of-the-art EMG teleoperation methods when applied to a
non-anthropomorphic, multi-DOF robot hand.
