DiPGrasp：面向高效可微抓取规划的并行局部搜索
DiPGrasp: Parallel Local Searching for Efficient Differentiable Grasp Planning
AI总结：
本文提出独立、快速、可微且适配不同自由度夹爪的 DiPGrasp，通过并行采样、碰撞处理和基于梯度的力封闭指标优化提升抓取搜索效率，并验证其在数据集构建、掩码条件规划、位姿优化及真实机器人上的效果。
摘要：
抓取规划是机器人操作中的一项重要任务。尽管该领域已有丰富研究，但尚未有一种独立、快速且可微的抓取规划器，能够适配不同自由度（DOF）的机器人夹爪。在本工作中，我们提出了 DiPGrasp，一种满足上述所有目标的抓取规划器。DiPGrasp 采用力封闭几何表面匹配抓取质量指标，并针对该指标使用基于梯度的优化方案，同时考虑并行采样和碰撞处理。这不仅大幅加速了物体表面上的抓取搜索过程，也使其具备可微性。我们将 DiPGrasp 应用于三个场景，即抓取数据集构建、掩码条件规划和位姿优化。在数据集生成方面，作为独立规划器，DiPGrasp 相比若干经典规划器在速度和质量上具有明显优势。在掩码条件规划方面，它能够将三维感知模型即时转化为三维抓取检测模型。作为位姿优化器，它既可以优化神经网络给出的粗略抓取预测，也可以优化神经网络参数。最后，我们使用 Barrett hand 和 Schunk SVH 五指手开展了真实世界实验。视频和补充材料可在我们的网站查看：https://dipgrasp.robotflow.ai。
英文摘要：
Grasp planning is an important task for robotic manipulation. Though it is a richly studied area, a standalone, fast, and differentiable grasp planner that can work with robot grippers of different DOFs has not been reported. In this work, we present DiPGrasp, a grasp planner that satisfies all these goals. DiPGrasp takes a force-closure geometric surface matching grasp quality metric. It adopts a gradient-based optimization scheme on the metric, which also considers parallel sampling and collision handling. This not only drastically accelerates the grasp search process over the object surface but also makes it differentiable. We apply DiPGrasp to three applications, namely grasp dataset construction, mask-conditioned planning, and pose refinement. For dataset generation, as a standalone planner, DiPGrasp has clear advantages over speed and quality compared with several classic planners. For mask-conditioned planning, it can turn a 3D perception model into a 3D grasp detection model instantly. As a pose refiner, it can optimize the coarse grasp prediction from the neural network, as well as the neural network parameters. Finally, we conduct real-world experiments with the Barrett hand and Schunk SVH 5-finger hand. Video and supplementary materials can be viewed on our website: \url{https://dipgrasp.robotflow.ai}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
