提出可执行的非抓握操作规划框架，让机械臂更智能地处理难抓物体。
LDHP: Library-Driven Hierarchical Planning for Non-prehensile Dexterous Manipulation
- 分层设计：上层规划物体运动路径，下层生成可行抓取序列
- 通过碰撞检测与静力学验证确保动作真实可执行
- 无需重新设计即可跨任务、跨形状迁移，适合真实机器人部署
非抓握操作在处理细长、大型或难以抓取的物体时至关重要。以往基于规划或搜索的方法常依赖人工设计，或忽略夹爪特性生成不可实现的动作；基于训练的方法则数据消耗大，泛化能力差。我们提出库驱动的分层规划器（LDHP），将可执行性作为核心目标：顶层接触状态规划器使用MoveObject原语生成物体位姿路径，底层抓取规划器使用AdjustGrasp原语合成可行抓取序列；可行性通过碰撞检查和准静态力学验证，接触敏感段通过有界二分法优化恢复。该夹爪感知的分解策略解耦了物体运动与抓取可实现性，构建了任务无关的通用流程，可在不重新设计的前提下跨任务与几何变化迁移，并为可选学习先验提供清晰接口。真实机器人实验在零移动抬升和插槽插入任务中均表现出一致执行效果和对形状与环境变化的鲁棒性。
原文摘要 · Abstract (English)
Non-prehensile manipulation is essential for handling thin, large, or otherwise ungraspable objects in unstructured settings. Prior planning and search-based methods often rely on ad-hoc manual designs or generate physically unrealizable motions by ignoring critical gripper properties, while training-based approaches are data-intensive and struggle to generalize to novel, out-of-distribution tasks. We propose a library-driven hierarchical planner (LDHP) that makes executability a first-class design goal: a top-tier contact-state planner proposes object-pose paths using MoveObject primitives, and a bottom-tier grasp planner synthesizes feasible grasp sequences with AdjustGrasp primitives; feasibility is certified by collision checks and quasi-static mechanics, and contact-sensitive segments are recovered via a bounded dichotomy refinement. This gripper-aware decomposition decouples object motion from grasp realizability, yields a task-agnostic pipeline that transfers across manipulation tasks and geometric variations without re-design, and exposes clean hooks for optional learned priors. Real-robot studies on zero-mobility lifting and slot insertion demonstrate consistent execution and robustness to shape and environment changes.
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