IPC-GraspSim: Reducing the Sim2Real Gap for Parallel-Jaw Grasping with the Incremental Potential Contact Model
Chung Min Kim, Michael Danielczuk, Isabella Huang, Ken Goldberg

TL;DR
IPC-GraspSim is a novel simulation framework that models the deformation of compliant jaw tips to significantly reduce the Sim2Real gap in robotic grasping, outperforming existing models in accuracy.
Contribution
Introduces IPC-GraspSim, extending Incremental Potential Contact to accurately simulate compliant jaw deformation, improving grasp prediction accuracy over existing models.
Findings
Higher precision and recall in grasp robustness prediction (F1=0.85)
Outperforms analytic and dynamic simulators in accuracy
Increases F1 score by up to 0.20 over baselines
Abstract
Accurately simulating whether an object will be lifted securely or dropped during grasping is a longstanding Sim2Real challenge. Soft compliant jaw tips are almost universally used with parallel-jaw robot grippers due to their ability to increase contact area and friction between the jaws and the object to be manipulated. However, interactions between the compliant surfaces and rigid objects are notoriously difficult to model. We introduce IPC-GraspSim, a novel grasp simulator that extends Incremental Potential Contact (IPC) - a highly accurate collision + deformation model developed in 2020 for computer graphics. IPC-GraspSim models both the dynamics and the deformation of compliant jaw tips to reduce Sim2Real gap for robot grasping. We evaluate IPC-GraspSim using a set of 2,000 physical grasps across 16 adversarial objects where analytic models perform poorly. In comparison to both…
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Taxonomy
TopicsRobot Manipulation and Learning · Muscle activation and electromyography studies · Motor Control and Adaptation
