TY - GEN
T1 - SoGraB
T2 - 8th IEEE International Conference on Soft Robotics, RoboSoft 2025
AU - Greenland, Benjamin G.
AU - Pinskier, Josh
AU - Wang, Xing
AU - Nguyen, Daniel
AU - Shi, Ge
AU - Bandyopadhyay, Tirthankar
AU - Chung, Jen Jen
AU - Howard, David
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The need for industrially relevant tools to safely handle delicate and deformable goods has driven a recent explosion in soft robotic gripper designs. However, there is currently no meaningful way to compare different designs. No commonly available, standardised evaluation protocol exists to assess the performance of soft grippers. This work introduces the Soft Grasping Benchmarking and Evaluation (SoGraB) method to evaluate grasp quality. It quantifies object deformation, and hence grasp quality, by measuring the Density-Aware Chamfer Distance (DCD) between point clouds of soft objects recorded before and after grasping. Through extensive experimentation, we demonstrate the method's ability to evaluate the quality of soft grasps, rank different gripper designs, select soft grippers for complex grasping tasks, and benchmark them for comparison against future designs. We believe SoGraB can be a standard for grasp evaluation and invite future users to contribute by benchmarking their own soft designs against our baselines or adding objects to the dataset.
AB - The need for industrially relevant tools to safely handle delicate and deformable goods has driven a recent explosion in soft robotic gripper designs. However, there is currently no meaningful way to compare different designs. No commonly available, standardised evaluation protocol exists to assess the performance of soft grippers. This work introduces the Soft Grasping Benchmarking and Evaluation (SoGraB) method to evaluate grasp quality. It quantifies object deformation, and hence grasp quality, by measuring the Density-Aware Chamfer Distance (DCD) between point clouds of soft objects recorded before and after grasping. Through extensive experimentation, we demonstrate the method's ability to evaluate the quality of soft grasps, rank different gripper designs, select soft grippers for complex grasping tasks, and benchmark them for comparison against future designs. We believe SoGraB can be a standard for grasp evaluation and invite future users to contribute by benchmarking their own soft designs against our baselines or adding objects to the dataset.
UR - https://www.scopus.com/pages/publications/105008416959
UR - https://ieeexplore.ieee.org/xpl/conhome/11020754/proceeding
UR - https://robosoft2025.org/
U2 - 10.1109/RoboSoft63089.2025.11020918
DO - 10.1109/RoboSoft63089.2025.11020918
M3 - Conference contribution
AN - SCOPUS:105008416959
T3 - 2025 IEEE 8th International Conference on Soft Robotics, RoboSoft 2025
SP - 1
EP - 6
BT - 2025 IEEE 8th International Conference on Soft Robotics, RoboSoft 2025
A2 - Della Santina, Cosimo
A2 - Beccal, Lucia
A2 - Rodrigue, Hugo
A2 - Truby, Ryan
A2 - Blumenschein, Laura
A2 - Olson, Gina
A2 - Ozaan, Onur
A2 - Maiolino, Perla
A2 - Renda, Federica
A2 - Paik, Jamie
A2 - Clanchetti, Matteo
PB - IEEE, Institute of Electrical and Electronics Engineers
Y2 - 22 April 2025 through 26 April 2025
ER -