Creation of Novel Soft Robot Designs using Generative AI

TL;DR

This paper demonstrates how generative AI, specifically a latent diffusion model, can be used to create innovative soft robot designs, addressing traditional challenges in soft robotics development.

Contribution

It introduces a novel application of generative AI for designing soft robotic actuators, utilizing a dataset and transfer learning to improve design generation.

Findings

Generated diverse soft actuator designs with high novelty

Improved model performance through transfer learning and data augmentation

Showed potential of AI to accelerate soft robotics innovation

Abstract

Soft robotics has emerged as a promising field with the potential to revolutionize industries such as healthcare and manufacturing. However, designing effective soft robots presents challenges, particularly in managing the complex interplay of material properties, structural design, and control strategies. Traditional design methods are often time-consuming and may not yield optimal designs. In this paper, we explore the use of generative AI to create 3D models of soft actuators. We create a dataset of over 70 text-shape pairings of soft pneumatic robot actuator designs, and adapt a latent diffusion model (SDFusion) to learn the data distribution and generate novel designs from it. By employing transfer learning and data augmentation techniques, we significantly improve the performance of the diffusion model. These findings highlight the potential of generative AI in designing complex soft robotic systems, paving the way for future advancements in the field.