  During in-hand manipulation, robots must be able to continuously estimate the
pose of the object in order to generate appropriate control actions. The
performance of algorithms for pose estimation hinges on the robot's sensors
being able to detect discriminative geometric object features, but previous
sensing modalities are unable to make such measurements robustly. The robot's
fingers can occlude the view of environment- or robot-mounted image sensors,
and tactile sensors can only measure at the local areas of contact. Motivated
by fingertip-embedded proximity sensors' robustness to occlusion and ability to
measure beyond the local areas of contact, we present the first evaluation of
proximity sensor based pose estimation for in-hand manipulation. We develop a
novel two-fingered hand with fingertip-embedded optical time-of-flight
proximity sensors as a testbed for pose estimation during planar in-hand
manipulation. Here, the in-hand manipulation task consists of the robot moving
a cylindrical object from one end of its workspace to the other. We
demonstrate, with statistical significance, that proximity-sensor based pose
estimation via particle filtering during in-hand manipulation: a) exhibits 50%
lower average pose error than a tactile-sensor based baseline; b) empowers a
model predictive controller to achieve 30% lower final positioning error
compared to when using tactile-sensor based pose estimates.
