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Collecting manipulation demonstrations with robotic hardware is tedious - andthus difficult to scale. Recording data on robot hardware ensures that it is inthe appropriate format for Learning from Demonstrations (LfD) methods. Bycontrast, humans are proficient manipulators, and recording their actions wouldbe easy to scale, but it is challenging to use that data format with LfDmethods. The question we explore is whether there is a method to collect datain a format that can be used with LfD while retaining some of the attractivefeatures of recording human manipulation. We propose equipping humans withhand-held, hand-actuated parallel grippers and a head-mounted camera to recorddemonstrations of manipulation tasks. Using customised and reproduciblegrippers, we collect an initial dataset of common manipulation tasks. We showthat there are tasks that, against our initial intuition, can be performedusing parallel grippers. Qualitative insights are obtained regarding the impactof the difference in morphology on LfD by comparing the strategies used tocomplete tasks with human hands and grippers. Our data collection methodbridges the gap between robot- and human-native manipulation demonstration. Bymaking the design of our gripper prototype available, we hope to reduce otherresearchers effort to collect manipulation data.

On Hand-Held Grippers and the Morphological Gap in Human Manipulation Demonstration