让灵巧手在动态使用工具时保持稳定抓握
Grasp to Act: Dexterous Grasping for Tool Use in Dynamic Settings
- 结合物理优化与强化学习，动态调整抓握姿态
- 在5种动态任务中实现零样本跨域迁移，抓握滑动减少
- 适合需要高精度操作的机器人应用，如工业装配
灵巧手在动态力（如冲击、扭矩、持续阻力）下的稳定抓握仍具挑战，现有方法多关注静态几何稳定性，外部力作用时易失效。本文提出Grasp-to-Act，融合基于物理的抓握优化与强化学习驱动的抓握自适应机制，通过人类示范生成鲁棒抓握配置，并利用残差控制器实时修正关节以防止手中滑移，同时跟踪物体轨迹。该方法在五类动态工具任务（锤击、锯切、切割、搅拌、舀取）中实现零样本仿真到现实的迁移，在16自由度灵巧手上显著降低平移与旋转滑移，任务完成率最高，验证了在接触密集、动态复杂的场景下稳定功能抓握的有效性。
原文摘要 · Abstract (English)
Achieving robust grasping with dexterous hands remains challenging, especially when manipulation involves dynamic forces such as impacts, torques, and continuous resistance--situations common in real-world tool use. Existing methods largely optimize grasps for static geometric stability and often fail once external forces arise during manipulation. We present Grasp-to-Act, a hybrid system that combines physics-based grasp optimization with reinforcement-learning-based grasp adaptation to maintain stable grasps throughout functional manipulation tasks. Our method synthesizes robust grasp configurations informed by human demonstrations and employs an adaptive controller that residually issues joint corrections to prevent in-hand slip while tracking the object trajectory. Grasp-to-Act enables robust zero-shot sim-to-real transfer across five dynamic tool-use tasks--hammering, sawing, cutting, stirring, and scooping--consistently outperforming baselines. Across simulation and real-world hardware trials with a 16-DoF dexterous hand, our method reduces translational and rotational in-hand slip and achieves the highest task completion rates, demonstrating stable functional grasps under dynamic, contact-rich conditions.
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