发表机构
CSIRO Robotics（澳大利亚联邦科学与工业研究组织机器人部门）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于图的软体夹爪表示与多目标多样性驱动遗传优化框架，通过多抓取场景优化，实现对新物体的涌现泛化与鲁棒性提升。
AI 中文摘要
在从农业收割到实验室及家庭自动化等应用中，对多样化物体的有效操作至关重要。虽然软体机器人固有的柔顺性非常适合这一挑战，但由于连续介质力学的巨大设计空间以及过拟合特定场景的风险，设计能够跨任务泛化的夹爪仍然困难。我们提出了一种基于图的表示空间来描述软体结构和机构，并耦合了一个多目标、多样性驱动的遗传优化框架，该框架在设计过程中明确促进解决方案的多样性。在优化过程中使用多种抓取场景，我们研究了任务多样性如何影响对未见物体和接触条件的泛化能力的涌现。我们的结果表明，在足够多样化的抓取案例集合上进行优化会导致设计产生涌现泛化能力，与针对特定任务的解决方案相比，在新场景上表现出更强的鲁棒性。这些发现表明，多样性驱动的优化为通用软体夹爪提供了一条原则性路径，与软体机器人的适应性本质相一致。
英文摘要
Effective manipulation across diverse objects is critical for applications ranging from agricultural harvesting to laboratory and domestic automation. While the inherent compliance of soft robotics is well suited to this challenge, designing grippers that generalize across tasks remains difficult due to the vast design space of continuum mechanics and the risk of overfitting to specific scenarios. We propose a graph-based design space for representing soft structures and mechanisms, coupled with a multi-objective, diversity-driven genetic optimization framework that explicitly promotes solution variety throughout the design process. Using multiple grasping scenarios during optimization, we study how task diversity influences the emergence of generalization to unseen objects and contact conditions. Our results show that optimization over a sufficiently diverse set of grasping cases leads to designs with emergent generalization, exhibiting improved robustness compared to task-specific solutions on novel scenarios. These findings suggest that diversity-driven optimization offers a principled pathway toward general-purpose soft grippers, aligned with the adaptable nature of soft robotics.
