Zobrazeno 1 - 10
of 559
pro vyhledávání: '"Wyffels P."'
Tactile sensing can enable robots to perform complex, contact-rich tasks. Magnetic sensors offer accurate three-axis force measurements while using affordable materials. Calibrating such a sensor involves either manual data collection, or automated p
Externí odkaz:
http://arxiv.org/abs/2405.18582
Assistive robots should be able to wash, fold or iron clothes. However, due to the variety, deformability and self-occlusions of clothes, creating robot systems for cloth manipulation is challenging. Synthetic data is a promising direction to improve
Externí odkaz:
http://arxiv.org/abs/2401.01734
Autor:
Proesmans, Remko, wyffels, Francis
The development of tactile sensing is expected to enhance robotic systems in handling complex objects like deformables or reflective materials. However, readily available industrial grippers generally lack tactile feedback, which has led researchers
Externí odkaz:
http://arxiv.org/abs/2309.05792
Autor:
Proesmans, Remko, wyffels, Francis
The development of tactile sensing and its fusion with computer vision is expected to enhance robotic systems in handling complex tasks like deformable object manipulation. However, readily available industrial grippers typically lack tactile feedbac
Externí odkaz:
http://arxiv.org/abs/2306.05902
Autor:
Lips, Thomas, wyffels, Francis
Publikováno v:
IEEE International Conference on Robotics and Automation 2O23 (ICRA 2023) - workshop Embracing Contacts
Robots that assist humans will need to interact with articulated objects such as cabinets or microwaves. Early work on creating systems for doing so used proprioceptive sensing to estimate joint mechanisms during contact. However, nowadays, almost al
Externí odkaz:
http://arxiv.org/abs/2305.09584
Autor:
De Roovere, Peter, Daems, Rembert, Croenen, Jonathan, Bourgana, Taoufik, de Hoog, Joris, Wyffels, Francis
We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consis
Externí odkaz:
http://arxiv.org/abs/2208.09829
We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our dataset contains both real-world and synthetic multi-vi
Externí odkaz:
http://arxiv.org/abs/2208.04052
Publikováno v:
Neurocomputing 573 (2024): 127175
We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. We show that interpreting this state as Cartesian coordinates, coupled with e
Externí odkaz:
http://arxiv.org/abs/2206.11030
Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks (CNN) can b
Externí odkaz:
http://arxiv.org/abs/2205.06714
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 21 (2024)
The increasing number of robots and the rising cost of electricity have spurred research into energy-reducing concepts in robotics. One such concept, elastic actuation, introduces compliant elements such as springs into the robot structure. This arti
Externí odkaz:
https://doaj.org/article/3be5077546be44ecb1d5d3706878767c