  Teleoperation is often limited by the ability of an operator to react and
predict the behavior of the robot as it interacts with the environment. For
example, to grasp small objects on a table, the teleoperator needs to predict
the position of the fingertips before the fingers are closed to avoid them
hitting the table. For that reason, we developed the F1 hand, a single-motor
gripper that facilitates teleoperation with the use of a fixed finger. The hand
is capable of grasping objects as thin as a paper clip, and as heavy and large
as a cordless drill. The applicability of the hand can be expanded by replacing
the fixed finger with different shapes. This flexibility makes the hand highly
versatile while being easy and cheap to develop. However, due to the atypical
asymmetric structure and actuation of the hand usual grasping strategies no
longer apply. Thus, we propose a controller that approximates actuation
symmetry by using the motion of the whole arm. The F1 hand and its controller
are compared side-by-side with the original Toyota Human Support Robot (HSR)
gripper in teleoperation using 22 objects from the YCB dataset in addition to
small objects. The grasping time and peak contact forces could be decreased by
20% and 70%, respectively while increasing success rates by 5%. Using an
off-the-shelf grasp pose estimator for autonomous grasping, the system achieved
similar success rates to the original HSR gripper, at the order of 80%.
