ReefFlex: A Generative Design Framework for Soft Robotic Grasping of Organic and Fragile objects

2026-02-09

AI Summary

Coral reef restoration is hindered by the lack of safe and robust manipulation tools capable of handling fragile and morphologically diverse corals. This work proposes a generative soft robotic finger design framework that, for the first time, integrates generative design with motion primitive encoding to reduce heterogeneous grasping tasks to a small set of solvable motion primitives. By formulating a multi-objective optimization problem, the framework automatically generates soft end-effectors tailored for complex marine environments. The resulting design significantly improves grasp success rate, disturbance rejection, and positioning accuracy while minimizing handling-induced damage. The approach has been successfully deployed in automated onshore coral nursery operations, offering a generalizable and scalable solution for ecological restoration tasks.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Climate change, invasive species and human activities are currently damaging the world's coral reefs at unprecedented rates, threatening their vast biodiversity and fisheries, and reducing coastal protection. Solving this vast challenge requires scalable coral regeneration technologies that can breed climate-resilient species and accelerate the natural regrowth processes; actions that are impeded by the absence of safe and robust tools to handle the fragile coral. We investigate ReefFlex, a generative soft finger design methodology that explores a diverse space of soft fingers to produce a set of candidates capable of safely grasping fragile and geometrically heterogeneous coral in a cluttered environment. Our key insight is encoding heterogeneous grasping into a reduced set of motion primitives, creating a simplified, tractable multi-objective optimisation problem. To evaluate the method, we design a soft robot for reef rehabilitation, which grows and manipulates coral in onshore aquaculture facilities for future reef out-planting. We demonstrate ReefFlex increases both grasp success and grasp quality (disturbance resistance, positioning accuracy) and reduces in adverse events encountered during coral manipulation compared to reference designs. ReefFlex, offers a generalisable method to design soft end-effectors for complex handling and paves a pathway towards automation in previously unachievable domains like coral handling for restoration.

Problem

• soft robotic grasping
• fragile objects
• coral handling
• generative design
• reef restoration

Innovation

• generative design
• soft robotics
• motion primitives
• multi-objective optimization
• fragile object manipulation