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

TL;DR

This paper introduces LDHP, a hierarchical planning framework for non-prehensile manipulation that emphasizes gripper feasibility, enabling robust, transferable manipulation across diverse tasks and objects in unstructured environments.

Contribution

LDHP presents a novel library-driven hierarchical planner that decouples object motion from grasp feasibility, improving transferability and robustness in non-prehensile manipulation tasks.

Findings

Successful real-robot experiments on lifting and slot insertion

Robustness to shape and environment variations

Transferability across different manipulation tasks

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.