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Graph-Based Design of Soft Grippers with Multi-Objective Quality-Diversity Optimisation
Andre Farinha, Ge Shi, Harry Bowman +3
Effective manipulation across diverse objects is critical for applications ranging from agricultural harvesting to laboratory and domestic automation. While the inherent compliance…
SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation
Benjamin G. Greenland, Josh Pinskier, Xing Wang +5
Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised e…
DexGrip: Multi-modal Soft Gripper with Dexterous Grasping and In-hand Manipulation Capacity
Xing Wang, Liam Horrigan, Josh Pinskier +6
The ability of robotic grippers to not only grasp but also re-position and re-orient objects in-hand is crucial for achieving versatile, general-purpose manipulation. While recent…
PINN-Ray: A Physics-Informed Neural Network to Model Soft Robotic Fin Ray Fingers
Xing Wang, Joel Janek Dabrowski, Josh Pinskier +4
Modelling complex deformation for soft robotics provides a guideline to understand their behaviour, leading to safe interaction with the environment. However, building a surrogate…
A 'MAP' to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization
Yue Xie, Josh Pinskier, Lois Liow +2
Soft robotics has emerged as the standard solution for grasping deformable objects, and has proven invaluable for mobile robotic exploration in extreme environments. However, despi…
Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM
Yue Xie, Xing Wang, Fumiya Iida +1
Computational design can excite the full potential of soft robotics that has the drawbacks of being highly nonlinear from material, structure, and contact. Up to date, enthusiastic…