Robots control dynamic flexible objects at multiple grasp points reliably
Manipulation with Stability Guarantees: Linear Deformable Objects with Non-negligible Physical Response Grasped at Multiple Location
Robotics
Summary
Manipulating flexible objects like hoses or wires is tough because they move in complex ways that slow or light objects don't. The authors created a method that helps robots control these objects more precisely by understanding their movements and adjusting forces at multiple hold points. Their approach uses a detailed mathematical model and feedback control to guide the object into a desired shape steadily. They tested this method using simulations and real-time experiments to show it works well in practice.
What this means in practice
- •For roboticists: Manipulate cables, hoses, or wiring harnesses with dynamic control accounting for object flexibility and multiple grasp points.
- •For industrial automation engineers: Design manufacturing systems that handle flexible line-shaped components more reliably using dynamic modeling and multi-point force control.
Authors
Daniel Feliu-Talegon, Cosimo Della Santina
Abstract
Most research on the manipulation of deformable objects focuses on lightweight systems with negligible mechanical response, effectively restricting attention to quasi-static regimes. This assumption excludes a broad class of practically relevant objects, such as hoses, pipes, and wiring harnesses, whose dynamics cannot be ignored during manipulation. In this work, we address this limitation by introducing a closed-loop control architecture that explicitly accounts for object dynamics and recasts manipulation as a shape-regulation problem. Control is achieved by modulating forces and torques applied at multiple fixed points along the object. This approach builds on three methodological contributions: a fully dynamic model of linear deformable objects based on discrete strain parameterizations; an extension of the notion of actuation coordinates to SE(3), yielding a structured and inherently underactuated control architecture; and nonlinear feedback strategies providing explicit conditions for steady-state convergence to desired configurations. Extensive simulations on representative manipulation tasks demonstrate the performance gains enabled by the proposed modelbased formulation. We finally validate the approach experimentally through a real-time closed-loop implementation with online shape estimation, confirming its practical feasibility and effectiveness