11 accepted papers
Estimating the stiffness of Deformable Linear Objects (DLOs) is crucial for robust manipulation. Inferring this hidden property depends heavily on the physical interaction strategy. Through a 1D CNN-based analysis of predefined probing modes, we first demonstrate that boundary constraints and grasp …
The automation of Deformable Linear Object (DLO) manipulation remains a key challenge in industrial production. While prior works demonstrated reliable wire terminal insertion using vision and tactile sensing, they typically assume a fixed connector pose. This paper presents a dual-arm robotic syste…
Robotic agri-food manipulation remains challenging because food items vary substantially in geometry, compliance, mass distribution, and surface properties, while their fragile nature makes grasping sensitive to small pose errors. This work presents a compact simulation-based study of how grasp topo…
Reliable grasp point selection on deformable linear objects, such as cables, requires not only accurate depth estimation but also awareness of prediction reliability. We present a five-stage stereo network for joint disparity, semantic, and uncertainty estimation, and use the predicted uncertainty t…
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…
The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our everyday life is massive. However, the applicability of robotic solutions to DLOs is still marginal due to the many challenges involved in their perception. In this letter, a methodology to generate datasets from
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
Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, ho
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