构建首个统一软硬耦合抓取仿真数据集，支持高效模拟复杂抓握场景。
GRIP: A General Robotic Incremental Potential Contact Simulation Dataset for Unified Deformable-Rigid Coupled Grasping
- 基于优化的IPC物理引擎，实现软硬物体交互的快速无碰撞仿真
- 生成100,000种抓握姿态与1,200类物体的交互数据，提速48倍
- 适用于神经网络抓取生成与应力场预测，适合研究柔性机器人抓取
抓取是机器人操作的基础，近年来大规模抓取数据集为学习方法提供了关键训练数据与评估基准。然而，由于缺乏可扩展、鲁棒的仿真流程，现有数据集普遍排除可变形体，限制了柔性夹爪与软性操作对象的通用模型发展。为此，本文提出GRIP——一种面向通用抓取任务的通用机器人增量势接触仿真数据集。GRIP采用优化的增量势接触（IPC）仿真器，在多环境场景下生成数据，相比传统方法实现最高48倍速度提升，同时确保柔性夹爪与可变形物体间的高效、无交叠、无反向求解仿真。全自动化流程生成并评估了1,200类物体与10万次抓握姿态的多样化交互，涵盖软质与刚性夹爪。该数据集可用于神经抓取生成与应力场预测等应用。
原文摘要 · Abstract (English)
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.
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