Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments

TL;DR

This paper introduces a hierarchical deformation planning and neural tracking framework for deformable linear objects in constrained environments, improving global planning and local deformation accuracy.

Contribution

It presents a novel combined approach of homotopic path-based planning and neural model predictive control for DLO manipulation in complex, obstacle-rich settings.

Findings

Effective in complex constrained environments

Accurate deformation tracking demonstrated

Improved planning and control performance

Abstract

Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning and robust deformation tracking. In this work, we propose a novel framework for DLO manipulation in constrained environments. This framework combines hierarchical deformation planning with neural tracking, ensuring reliable performance in both global deformation synthesis and local deformation tracking. Specifically, the deformation planner begins by generating a spatial path set that inherently satisfies the homotopic constraints associated with DLO keypoint paths. Next, a path-set-guided optimization method is applied to synthesize an optimal temporal deformation sequence for the DLO. In manipulation execution, a neural model predictive control approach, leveraging a data-driven deformation model, is designed to accurately track the planned DLO deformation sequence. The effectiveness of the proposed framework is validated in extensive constrained DLO manipulation tasks.