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This research applies multi-objective quality-diversity optimization within a graph-based framework to design soft robotic grippers capable of generalizing across unseen objects and contact conditions. By explicitly promoting solution variety during the optimization process, the study demonstrates that task diversity drives the emergence of robust, adaptable designs suitable for agriculture and domestic automation. The work represents a significant methodological advance in machine learning-driven robotics, specifically addressing the challenge of overfitting in continuum mechanics design spaces.
Graph-Based Design of Soft Grippers with Multi-Objective Quality-Diversity Optimisation
arXiv:2609.20087v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO)
arXiv:2609.20087 [cs.RO]
(or arXiv:2609.20087v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.20087 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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