ConGraspXL：可控约束条件下的灵巧抓取运动合成
ConGraspXL: Controllable Constraint-Conditioned Dexterous Grasping Motion Synthesis
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中文总结 AI 辅助
针对灵巧抓取在任务约束下缺乏可控性的问题，提出ConGraspXL，通过层次化约束公式、掩码残差接口和动态手中心，实现精确灵活的约束条件抓取运动合成，并保持泛化能力。
中文摘要 AI 辅助
灵巧抓取通常针对特定任务进行，导致出现异构约束，例如特定的接近方向、期望的接触区域、指定的手腕轨迹和功能性手部姿态。我们之前的工作GraspXL实现了针对多样物体和手部形态的可扩展抓取运动合成，但在这种多种任务驱动约束下的合成缺乏可控性。在本文中，我们提出ConGraspXL，它扩展了GraspXL，实现了可控制的约束条件抓取运动合成，以适应多样化的任务驱动约束及其组合。我们引入了一种层次化约束公式，通过掩码残差接口实现灵活的约束组合，并通过动态手中心和前馈手腕引导提高控制精度。在不丧失GraspXL强大泛化能力的情况下，ConGraspXL为各种单独约束及其组合提供了精确而灵活的可控性，为下游应用（如全身抓取完成、功能性抓取和人体运动模仿）提供了即插即用的低级抓取控制器。
英文摘要
Dexterous grasping is usually conducted for specific tasks, leading to heterogeneous constraints such as specific approach directions, desired contact regions, specified wrist trajectories, and functional hand poses. Our previous work, GraspXL, achieves scalable grasping motion synthesis for diverse objects and hand morphologies, while lacking controllability for synthesis under such various task-driven constraints. In this paper, we propose ConGraspXL, which extends GraspXL with controllable constraint-conditioned grasp motion synthesis that accommodates diverse task-driven constraints and their combinations. We introduce a hierarchical constraint formulation, enable flexible constraint composition with a masked residual interface, and improve control precision with dynamic hand centers and feed-forward wrist guidance. Without losing the strong generalization capabilities of GraspXL, ConGraspXL enables precise and flexible controllability for various individual constraints and their combinations, providing a plug-and-play low-level grasp controller for downstream applications such as whole-body grasp completion, functional grasping, and human-motion imitation.
发表机构
- ETH Zürich（苏黎世联邦理工学院）
- HKUST (GZ)（香港科技大学（广州））
- HKUST（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
