Robotic perception and manipulation of deformable linear objects: A survey

Deformable linear objects (DLOs) are widely encountered in everyday life, taking forms such as plastic tubes, wires, ropes, and cables. They are prevalent across diverse settings, including industrial, domestic, and medical environments, as well as in outdoor applications like electric power lines, subaquatic cables, and aerial transport systems. These objects are termed deformable due to their ability to undergo significant shape changes under external forces, and linear because their length vastly exceeds their cross-sectional dimensions. Despite their importance and widespread presence, developing robotic systems capable of interacting with DLOs poses numerous challenges. This survey presents a comprehensive review of the state-of-the-art methods developed over the past decade to address these challenges. It covers key areas including physical and data-driven modeling techniques, simulation environments, perception approaches based on vision and tactile sensing, as well as strategies for estimation, planning, and control. It also reviews common manipulation tasks such as grasping, shaping, routing, knotting, suturing, and transport. The survey concludes with a critical discussion of current limitations and outlines promising directions for future research.

Authors

Institutions

Publication Details

Journal
The International Journal of Robotics Research
Published
2026-03-27
DOI
https://doi.org/10.1177/02783649261432253
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Robotic perception and manipulation of deformable linear objects: A survey

Alessio Caporali, Ignacio Cuiral-Zueco, Gianluca Palli, Gonzalo López-Nicolás
The International Journal of Robotics Research
Soft Robotics and Applications
article

Robotic perception and manipulation of deformable linear objects: A survey

Alessio Caporali, Ignacio Cuiral-Zueco, Gianluca Palli, Gonzalo López-Nicolás
article en

Abstract

Deformable linear objects (DLOs) are widely encountered in everyday life, taking forms such as plastic tubes, wires, ropes, and cables. They are prevalent across diverse settings, including industrial, domestic, and medical environments, as well as in outdoor applications like electric power lines, subaquatic cables, and aerial transport systems. These objects are termed deformable due to their ability to undergo significant shape changes under external forces, and linear because their length vastly exceeds their cross-sectional dimensions. Despite their importance and widespread presence, developing robotic systems capable of interacting with DLOs poses numerous challenges. This survey presents a comprehensive review of the state-of-the-art methods developed over the past decade to address these challenges. It covers key areas including physical and data-driven modeling techniques, simulation environments, perception approaches based on vision and tactile sensing, as well as strategies for estimation, planning, and control. It also reviews common manipulation tasks such as grasping, shaping, routing, knotting, suturing, and transport. The survey concludes with a critical discussion of current limitations and outlines promising directions for future research.

The International Journal of Robotics Research
Universidad de Zaragoza (ES), Sigma Clermont (FR), Instituto de Investigación Sanitaria Aragón (ES), University of Bologna (IT)
Sustainable cities and communities
Openalex Percentile: Top 7%
Soft Robotics and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.