A simulation-trained neural surrogate jointly optimizes stiffness distribution and grasp pose for a soft gripper, improving hardware grasp success over rigid and soft baselines.
EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
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abstract
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github.io/egad/
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Co-Design of Soft Gripper with Neural Physics
A simulation-trained neural surrogate jointly optimizes stiffness distribution and grasp pose for a soft gripper, improving hardware grasp success over rigid and soft baselines.