LDHP: Library-Driven Hierarchical Planning for Non-prehensile Dexterous Manipulation
This addresses manipulation challenges in unstructured settings for robotics, though it appears incremental as it builds on prior planning methods with a gripper-aware decomposition.
The paper tackled the problem of non-prehensile dexterous manipulation for thin or ungraspable objects by proposing a library-driven hierarchical planner (LDHP) that ensures executability, resulting in consistent execution and robustness to shape and environment changes in real-robot studies.
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.