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

2026-03-14

AI Summary

This work addresses the challenges in non-prehensile dexterous manipulation—namely, infeasible actions due to neglecting gripper physical constraints, poor generalization, and heavy reliance on large datasets or manual design—by proposing a gripper-aware hierarchical planning framework. Centered on executability, the approach decouples object motion planning from grasp feasibility: an upper layer plans object pose trajectories using MoveObject primitives, while a lower layer synthesizes feasible grasp sequences via AdjustGrasp primitives, with collision checking and quasi-static force analysis validating contact-sensitive segments. The method requires no task-specific redesign and supports transfer across tasks and geometric variations. Real-robot experiments on zero-displacement lifting and slot-insertion tasks demonstrate strong robustness and consistent execution performance.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• non-prehensile manipulation
• dexterous manipulation
• motion planning
• generalization
• gripper-aware planning

Innovation

• non-prehensile manipulation
• hierarchical planning
• gripper-aware planning
• quasi-static mechanics
• task-agnostic pipeline