Grasping is fundamental to robotic manipulation, and recent advances in large-scale grasping datasets have provided essential training data and evaluation benchmarks, accelerating the development of learning-based methods for robust object grasping. However, most existing datasets exclude deformable bodies due to the lack of scalable, robust simulation pipelines, limiting the development of generalizable models for compliant grippers and soft manipulands. To address these challenges, we present GRIP, a General Robotic Incremental Potential contact simulation dataset for universal grasping. GRIP leverages an optimized Incremental Potential Contact (IPC)-based simulator for multi-environment data generation, achieving up to 48x speedup while ensuring efficient, intersection- and inversion-free simulations for compliant grippers and deformable objects. Our fully automated pipeline generates and evaluates diverse grasp interactions across 1,200 objects and 100,000 grasp poses, incorporating both soft and rigid grippers. The GRIP dataset enables applications such as neural grasp generation and stress field prediction.
@article{arxiv.2503.05020,
title = {GRIP: A General Robotic Incremental Potential Contact Simulation Dataset for Unified Deformable-Rigid Coupled Grasping},
author = {Siyu Ma and Wenxin Du and Chang Yu and Ying Jiang and Zeshun Zong and Tianyi Xie and Yunuo Chen and Yin Yang and Xuchen Han and Chenfanfu Jiang},
journal= {arXiv preprint arXiv:2503.05020},
year = {2025}
}
Comments
We release GRIP to advance research in robotic manipulation, soft-gripper control, and physics-driven simulation at: https://bell0o.github.io/GRIP/