用可微分优化生成多样且稳定的精细抓握，提升机器人手灵活性。
GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping
- 基于QP的可微分能量模型定义力闭合约束
- 动态拒绝梯度步提升5700个物体的抓握多样性与稳定性
- 适合抓取预测、策略训练和任务规划研究者使用
灵巧机器人手因多指设计具备灵活适应性，可在复杂环境中实现多样化任务抓握。但要充分发挥其能力，需大量多样且高质量的抓握数据——用于点云抓取预测、操控策略训练或高层任务规划。现有数据生成方法依赖采样算法或简化力闭合分析，易收敛至力量型抓握，多样性不足。本文提出一种新方法，可生成大规模、多样且物理可行的抓握，涵盖捏持、三指精密抓握等精细操作。引入基于二次规划（QP）的可微分力闭合能量公式，并设计改进优化方法MALA*，通过能量分布动态拒绝梯度步以提升性能。在5,700个物体的DexGraspNet数据集上评估，显著提升抓握多样性与最终抓握预测稳定性。公开发布包含五种夹持器和三种抓握类型的新数据集及代码。
原文摘要 · Abstract (English)
Dexterous robotic hands enable versatile interactions due to the flexibility and adaptability of multi-fingered designs, allowing for a wide range of task-specific grasp configurations in diverse environments. However, to fully exploit the capabilities of dexterous hands, access to diverse and high-quality grasp data is essential -- whether for developing grasp prediction models from point clouds, training manipulation policies, or supporting high-level task planning with broader action options. Existing approaches for dataset generation typically rely on sampling-based algorithms or simplified force-closure analysis, which tend to converge to power grasps and often exhibit limited diversity. In this work, we propose a method to synthesize large-scale, diverse, and physically feasible grasps that extend beyond simple power grasps to include refined manipulations, such as pinches and tri-finger precision grasps. We introduce a rigorous, differentiable energy formulation of force closure, implicitly defined through a Quadratic Program (QP). Additionally, we present an adjusted optimization method (MALA*) that improves performance by dynamically rejecting gradient steps based on the distribution of energy values across all samples. We extensively evaluate our approach and demonstrate significant improvements in both grasp diversity and the stability of final grasp predictions. Finally, we provide a new, large-scale grasp dataset for 5,700 objects from DexGraspNet, comprising five different grippers and three distinct grasp types. Dataset and Code:https://graspqp.github.io/
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