  Lifting objects, whose mass may produce high wrist torques that exceed the
hardware strength limits, could lead to unstable grasps or serious robot
damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose
adaptation method, for picking up objects using a combination of exteroceptive
3D perception and proprioceptive force/torque sensor feedback. The method works
in two iterative stages to provide reliable and wrist torque efficient grasps.
Initially, a geometric object CoM is estimated from the input range data. In
the first stage, a set of hand-size handle grasps are localized on the object
and the closest to its CoM is selected for grasping. In the second stage, the
object is lifted using a single arm, while the force and torque readings from
the sensor on the wrist are monitored. Based on these readings, a displacement
to the new CoM estimation is calculated. The object is released and the process
is repeated until the wrist torque effort is minimized. The advantage of our
method is the blending of both exteroceptive (3D range) and proprioceptive
(force/torque) sensing for finding the grasp location that minimizes the wrist
effort, potentially improving the reliability of the grasping and the
subsequent manipulation task. We experimentally validate the proposed method by
executing a number of tests on a set of objects that include handles, using the
humanoid robot WALK-MAN.
