6 accepted papers
The robotic manipulation of deformable objects, such as clothes and fabric, is known as a complex task from both the perception and planning perspectives. Indeed, the stochastic nature of the underlying environment dynamics makes it an interesting research field for statistical learning approaches a…
The perception of Deformable Linear Objects (DLOs) is a challenging task due to their complex and ambiguous appearance, lack of discernible features, typically small sizes, and deformability. Despite these challenges, achieving a robust and effective segmentation of DLOs is crucial to introduce robo
Manipulating Deformable Linear Objects (DLOs) is a challenging task for a robotic system due to their unpredictable configuration, high-dimensional state space and complex nonlinear dynamics. This paper presents a framework addressing the manipulation of DLOs, specifically targeting the model-based
Deformable Objects (DOs), e.g. clothes, garments, cables, wires, and ropes, are pervasive in our everyday environment. Despite their importance and widespread presence, many limitations exist when deploying robotic systems to interact with DOs. One source of challenges arises from their complex perc…
This letter presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DLO3DS</i> , an approach for the 3D shapes estimation and tracking of Deformable Linear Objects (DLOs) such as cables, wires or plastic hoses, using a cheap and compact 2D vision se
In this paper, an approach for fast and accurate segmentation of Deformable Linear Objects (DLOs) named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FASTDLO</i> is presented. A deep convolutional neural network is employed for background segmentat