LDHP: Library-Driven Hierarchical Planning for Non-prehensile Dexterous Manipulation

• URL: http://arxiv.org/abs/2603.13844v1
• Date: Sat, 14 Mar 2026 08:54:49 GMT
• Title: LDHP: Library-Driven Hierarchical Planning for Non-prehensile Dexterous Manipulation
• Abstract summary: Non-prehensile manipulation is essential for handling thin, large, or otherwise ungraspable objects in unstructured settings.<n>Prior planning and search-based methods often rely on ad-hoc manual designs or generate physically unrealizable motions.<n>We propose a library-driven hierarchical planner (LDHP) that makes executability a first-class design goal.
• Score: 3.829270241398111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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