5 papers
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…
Grasp Synthesis Matching From Rigid To Soft Robot Grippers Using Conditional Flow Matching
Tanisha Parulekar, Ge Shi, Josh Pinskier +2
A representation gap exists between grasp synthesis for rigid and soft grippers. Anygrasp [1] and many other grasp synthesis methods are designed for rigid parallel grippers, and a…
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…
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation
Ge Shi, Zhili Yang
Dynamic scene understanding is one of the most conspicuous field of interest among computer vision community. In order to enhance dynamic scene understanding, pixel-wise segmentati…