  Collecting manipulation demonstrations with robotic hardware is tedious - and
thus difficult to scale. Recording data on robot hardware ensures that it is in
the appropriate format for Learning from Demonstrations (LfD) methods. By
contrast, humans are proficient manipulators, and recording their actions would
be easy to scale, but it is challenging to use that data format with LfD
methods. The question we explore is whether there is a method to collect data
in a format that can be used with LfD while retaining some of the attractive
features of recording human manipulation. We propose equipping humans with
hand-held, hand-actuated parallel grippers and a head-mounted camera to record
demonstrations of manipulation tasks. Using customised and reproducible
grippers, we collect an initial dataset of common manipulation tasks. We show
that there are tasks that, against our initial intuition, can be performed
using parallel grippers. Qualitative insights are obtained regarding the impact
of the difference in morphology on LfD by comparing the strategies used to
complete tasks with human hands and grippers. Our data collection method
bridges the gap between robot- and human-native manipulation demonstration. By
making the design of our gripper prototype available, we hope to reduce other
researchers effort to collect manipulation data.
