  Garments with the ability to provide kinesthetic force-feedback on-demand can
augment human capabilities in a non-obtrusive way, enabling numerous
applications in VR haptics, motion assistance, and robotic control. However,
designing such garments is a complex, and often manual task, particularly when
the goal is to resist multiple motions with a single design. In this work, we
propose a computational pipeline for designing connecting structures between
active components - one of the central challenges in this context. We focus on
electrostatic (ES) clutches that are compliant in their passive state while
strongly resisting elongation when activated. Our method automatically computes
optimized connecting structures that efficiently resist a range of pre-defined
body motions on demand. We propose a novel dual-objective optimization approach
to simultaneously maximize the resistance to motion when clutches are active,
while minimizing resistance when inactive. We demonstrate our method on a set
of problems involving different body sites and a range of motions. We further
fabricate and evaluate a subset of our automatically created designs against
manually created baselines using mechanical testing and in a VR pointing study.
