Coordinated Manipulation of Hybrid Deformable-Rigid Objects in Constrained Environments

TL;DR

This paper introduces a novel optimization-based planner for manipulating hybrid deformable-rigid objects in constrained spaces, leveraging a strain-based model and analytical gradients for efficiency and accuracy.

Contribution

It extends rigid-body formulations to hybrid deformable objects using a Cosserat rod model, enabling faster and more accurate manipulation planning in constrained environments.

Findings

Achieves up to 33x speedup over finite-difference methods.

Maintains an average deformation error of about 3 cm in experiments.

Outperforms sampling-based feasibility planners in effectiveness.

Abstract

Coordinated robotic manipulation of deformable linear objects (DLOs), such as ropes and cables, has been widely studied; however, handling hybrid assemblies composed of both deformable and rigid elements in constrained environments remains challenging. This work presents a quasi-static optimization-based manipulation planner that employs a strain-based Cosserat rod model, extending rigid-body formulations to hybrid deformable linear objects (hDLO). The proposed planner exploits the compliance of deformable links to maneuver through constraints while achieving task-space objectives for the object that are unreachable with rigid tools. By leveraging a differentiable model with analytically derived gradients, the method achieves up to a 33x speedup over finite-difference baselines for inverse kinetostatic(IKS) problems. Furthermore, the subsequent trajectory optimization problem, warm-started using the IKS solution, is only practically realizable via analytical derivatives. The proposed algorithm is validated in simulation on various hDLO systems and experimentally on a three-link hDLO manipulated in a constrained environment using a dual-arm robotic system. Experimental results confirm the planner's accuracy, yielding an average deformation error of approximately 3 cm (5% of the deformable link length) between the desired and measured marker positions. Finally, the proposed optimal planner is compared against a sampling-based feasibility planner adapted to the strain-based formulation. The results demonstrate the effectiveness and applicability of the proposed approach for robotic manipulation of hybrid assemblies in constrained environments.