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Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to…

机器人学 · 计算机科学 2022-08-30 Mingrui Yu , Kangchen Lv , Hanzhong Zhong , Shiji Song , Xiang Li

Autonomous wire harness assembly requires robots to manipulate complex branched cables with high precision and reliability. A key challenge in automating this process is predicting how these flexible and branched structures behave under…

机器人学 · 计算机科学 2025-05-07 Yizhou Chen , Xiaoyue Wu , Yeheng Zong , Yuzhen Chen , Anran Li , Bohao Zhang , Ram Vasudevan

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process.…

机器人学 · 计算机科学 2023-12-12 Dezhong Tong , Andrew Choi , Longhui Qin , Weicheng Huang , Jungseock Joo , M. Khalid Jawed

Manipulating a deformable linear object (DLO) such as wire, cable, and rope is a common yet challenging task due to their high degrees of freedom and complex deformation behaviors, especially in an environment with obstacles. Existing local…

机器人学 · 计算机科学 2025-12-02 Burak Aksoy , John Wen

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot…

机器人学 · 计算机科学 2023-09-15 Piotr Kicki , Michał Bidziński , Krzysztof Walas

Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long…

This work introduces an analytical approach for detecting and estimating external forces acting on deformable linear objects (DLOs) using only their observed shapes. In many robot-wire interaction tasks, contact occurs not at the…

机器人学 · 计算机科学 2026-02-03 Qi Jing Chen , Shilin Shan , Timothy Bretl , Quang-Cuong Pham

Robotic manipulation of deformable materials is a challenging task that often requires realtime visual feedback. This is especially true for deformable linear objects (DLOs) or "rods", whose slender and flexible structures make proper…

机器人学 · 计算机科学 2024-02-21 Andrew Choi , Dezhong Tong , Brian Park , Demetri Terzopoulos , Jungseock Joo , Mohammad Khalid Jawed

Deformable object manipulation (DOM) represents a critical challenge in robotics, with applications spanning healthcare, manufacturing, food processing, and beyond. Unlike rigid objects, deformable objects exhibit infinite dimensionality,…

机器人学 · 计算机科学 2026-02-27 Ryan Paul McKennaa , John Oyekan

The deformable linear objects (DLOs) are common in both industrial and domestic applications, such as wires, cables, ropes. Because of its highly deformable nature, it is difficult for the robot to reproduce human's dexterous skills on…

机器人学 · 计算机科学 2021-07-02 Mingrui Yu , Hanzhong Zhong , Fangxun Zhong , Xiang Li

Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. However, perception of these objects and the study of physical interaction with them is still a growing area. There have already been…

机器人学 · 计算机科学 2023-04-11 Azarakhsh Keipour , Mohammadreza Mousaei , Maryam Bandari , Stefan Schaal , Sebastian Scherer

Humans are able to manipulate Deformable Linear Objects (DLOs) such as cables and wires, with little or no visual information, relying mostly on force sensing. In this work, we propose a reduced DLO model which enables such blind…

机器人学 · 计算机科学 2023-10-11 Finn Süberkrüb , Rita Laezza , Yiannis Karayiannidis

Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill that humans use to dexterously manipulate DLOs, which requires…

Manipulating deformable objects arises in daily life and numerous applications. Despite phenomenal advances in industrial robotics, manipulation of deformable objects remains mostly a manual task. This is because of the high number of…

机器人学 · 计算机科学 2024-01-31 Burak Aksoy , John Wen

Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existing methods face challenges in handling complex large deformation tasks, especially those…

机器人学 · 计算机科学 2026-02-26 Zhaowei Liang , Song Wang , Zhao Jin , Shirui Wu , Dan Wu

This paper proposes a new control framework for manipulating soft objects. A Deep Reinforcement Learning (DRL) approach is used to make the shape of a deformable object reach a set of desired points by controlling a robotic arm which…

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…

机器人学 · 计算机科学 2026-03-16 Anees Peringal , Anup Teejo Mathew , Panagiotis liatsis , Federico Renda

Serially connected robots are promising candidates for performing tasks in confined spaces such as search-and-rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve…

Deformable linear object (DLO) manipulation is needed in many fields. Previous research on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper manipulation with fixed grasping positions. However, the…

机器人学 · 计算机科学 2023-12-27 Sun Zhaole , Jihong Zhu , Robert B. Fisher

Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remains a challenging open issue due to the high dimensionality…

机器人学 · 计算机科学 2023-05-03 Kangchen Lv , Mingrui Yu , Yifan Pu , Xin Jiang , Gao Huang , Xiang Li