Computational Design of Active Kinesthetic Garments

TL;DR

This paper introduces a computational pipeline for designing active kinesthetic garments with electrostatic clutches, enabling efficient resistance to multiple motions, validated through fabrication and user testing.

Contribution

It presents a novel dual-objective optimization method for automatically designing connecting structures in kinesthetic garments, improving resistance control and reducing manual design effort.

Findings

Automated designs outperform manual baselines in mechanical tests.

The method effectively resists multiple body motions on demand.

User studies show improved VR interaction with the designed garments.

Abstract

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.