Programming Passive Fingertip Deformation for Improved Grasping and Manipulation

Soft robots exhibit complex behaviors despite simple control, due to their inherently compliant hardware which passively deforms upon contact ± a concept commonly referred to as morphological computation. To fully determine the behavior of soft robots, not only their control software but also their passive behavior needs to be programmed. We show that deliberate programming of passive deformation in soft fingertips can significantly influence the grasping and manipulation performance of various robotic grippers. For this, the fingertips display strategically modulated compliance levels across their palmar surface, realized through adjustments to the local thickness of a lattice structure within their soft material, resulting in desired passive deformation. The grippers are operated by human participants, solving diverse tasks involving a variety of objects. We analyze 2025 human trials and show that the distinct passive behaviors programmed into the fingertips significantly affect grasping and manipulation performance. Furthermore, we discovered that specific compliance profiles consistently demonstrate superior performance, indicating that not merely inherent softness by itself, but a purposeful combination of varying compliance levels plays a pivotal role in successful soft interaction.