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.

翻译：软体机器人学作为一个前景广阔的领域，有望彻底改变医疗和制造业等行业。然而，设计有效的软体机器人面临挑战，特别是在管理材料属性、结构设计和控制策略的复杂相互作用方面。传统设计方法通常耗时且可能无法产生最优设计。本文探索使用生成式人工智能来创建软体执行器的三维模型。我们构建了一个包含70多组软体气动机器人执行器设计的文本-形状配对数据集，并采用潜在扩散模型（SDFusion）学习数据分布，进而生成新颖设计。通过运用迁移学习和数据增强技术，我们显著提升了扩散模型的性能。这些发现凸显了生成式人工智能在设计复杂软体机器人系统方面的潜力，为该领域的未来发展铺平了道路。