Title: EMG-Controlled Hand Teleoperation Using a Continuous Teleoperation Subspace,
Abstract: 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. We show that 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.