This work proposes a novel generative design tool for passive grippers—robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its orientation with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge by jointly optimizing the gripper shape and the insert trajectory to enlarge the set of objects that can be pasively grasped. We evaluate our method on a testing suite of 23 objects, all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Inspired by the true cost of repurposing infrastructures in assembly lines following the recent changes in demand early in the COVID-19 crisis, our work allows a cost effective solution to rapidly generate, fabricate, and deploy custom passive grippers on the existing robot arms for the new products in demand.

The NIH 3D Print Exchange is a public and open source repository for primarily 3D printable medical device designs with contributions from expert-amateur makers, engineers from industry and academia, and clinicians. In response to the COVID-19 pandemic, a collection was formed to foster creative submissions of low-cost, locally manufacturable personal protective equipment (PPE). To understand trends from this extraordinary occurrence of medical making, we performed a mixed-methods analysis of this collection. We used a combination of qualitative data from a thematic analysis and quantitative data from web scraped details of over 600 submissions. From this analysis, we found a disconnect between the NIH’s intention for the platform and how it was used. Instead of generating a diverse array of designs, the submission requirements and rating designations led to a rapid convergence of the design space. In this presentation, I present our findings for what we believe resulted in this disconnect and provide suggestions for how to improve upon the repository’s design. This work contributes valuable insights into the outcomes of distributed, community-based medical making and how platforms can support regulated maker activities in high-risk domains such as healthcare. Furthermore, many of our recommendations could be applied to non-health focused maker repositories such as Thingiverse and Instructables.