发表机构
BNRist（北京信息科学与技术国家研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在形状和位置不确定时灵巧抓取执行问题，提出触觉驱动模型预测控制器，强调多接触协调，有协调感知相分离等三个创新点，经仿真和实验验证，提高了抓取成功率并减少物体不必要移动。
AI 中文摘要
虽然近期研究主要集中在灵巧抓取姿态生成，但对计划抓取的执行关注较少。在形状和位置不确定情况下，开环执行常导致不协调接触。为此，本文提出一种触觉驱动的模型预测控制器，用于各种灵巧抓取的自适应精细执行。我们的方法强调接近和抓取阶段的多接触协调，有三个关键创新点。使用分析模型将接触力与机器人关节运动相关联以进行预测控制。我们的公式对抓取类型或接触配置没有限制，并与最先进的抓取姿态生成方法无缝集成。我们通过在三个机器人手上对478个物体进行15k次抓取的大规模模拟以及对8个物体进行的实际实验来验证该方法。结果表明，我们的方法实现了更高的抓取成功率并减少了不必要的物体移动。
英文摘要
While recent research has focused heavily on dexterous grasp pose generation, less attention has been devoted to the execution of planned grasps. Under shape and position uncertainty, open-loop execution often yields uncoordinated contacts, causing undesired in-hand object motion and even grasp failures. To address this, this paper proposes a tactile-driven model predictive controller for adaptive and delicate execution of diverse dexterous grasps. Our approach emphasizes multi-contact coordination across both approaching and grasping phases, with three key novelties: (i) coordination-aware phase separation, (ii) arm-hand coordination to compensate for position errors, and (iii) adaptive force coordination to increase contact forces in a balanced manner. An analytical model is employed to relate contact forces to robot joint motions for predictive control. Our formulation imposes no restrictions on grasp types or contact configurations and integrates seamlessly with state-of-the-art grasp pose generation methods. We validate the approach through large-scale simulations involving 15k grasps across 478 objects on three robotic hands, and real-world experiments on 8 objects. Results demonstrate that our method achieves higher grasp success rates and reduced undesired object movements.
CommentsAccepted by ICRA 2026. Best Poster Award at ICRA 2026 Workshop on Dexterity with Multifingered Hands: Hardware, Sensing, and Skills. Project Website: https://ada-grasp-ctrl.github.io/
