SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation

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 evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a novel protocol, the Soft Grasping Benchmarking and Evaluation (SoGraB) method, to evaluate grasping quality, which quantifies object deformation by using the Density-Aware Chamfer Distance (DCD) between point clouds of soft objects before and after grasping. We validated our protocol in extensive experiments, which involved ranking three FinRay gripper designs with a subset of the EGAD object dataset. The protocol appropriately ranked grippers based on object deformation information, validating the method’s ability to select soft grippers for complex grasping tasks and benchmark them for comparison against future designs.

Note: To maximise accuracy, the Analysis Pose should be selected as to minimise occlusions.

The scoring system evaluates grasp quality using three features, the grasp success, holding time and deformation. The deformation of the objects are determined by calculating the Density-Aware Chamfer Distance (DCD) between point clouds taken before and during the grasp. This metric ensures that the deformation is quantified accurately, enabling a fair comparison of different soft gripper designs.

Referenced from: Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion