Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Alenyà, Guillem"'
Autor:
Longhini, Alberta, Wang, Yufei, Garcia-Camacho, Irene, Blanco-Mulero, David, Moletta, Marco, Welle, Michael, Alenyà, Guillem, Yin, Hang, Erickson, Zackory, Held, David, Borràs, Júlia, Kragic, Danica
The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within th
Externí odkaz:
http://arxiv.org/abs/2407.01361
Autor:
Garcia-Camacho, Irene, Longhini, Alberta, Welle, Michael, Alenyà, Guillem, Kragic, Danica, Borràs, Júlia
Publikováno v:
2024 ICRA International Conference on Robotics and Automation (ICRA)
The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardising fabric properties like elasticity, stiffness, and friction. While the significance of these properties is evident in t
Externí odkaz:
http://arxiv.org/abs/2403.04608
This paper presents an approach to ensure conditions on Variable Impedance Controllers through the off-line tuning of the parameters involved in its description. In particular, we prove its application to term modulations defined by a Learning from D
Externí odkaz:
http://arxiv.org/abs/2209.10244
Publikováno v:
ECCV 2022
We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. In contrast to existing approaches that directly regress the parameters of a low-dimensional statistical body model (e.g. SMPL) from input images, we train
Externí odkaz:
http://arxiv.org/abs/2205.06254
Understanding of deformable object manipulations such as textiles is a challenge due to the complexity and high dimensionality of the problem. Particularly, the lack of a generic representation of semantic states (e.g., \textit{crumpled}, \textit{dia
Externí odkaz:
http://arxiv.org/abs/2203.11647
Publikováno v:
IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 5866-5873, July 2022
Benchmarking of robotic manipulations is one of the open issues in robotic research. An important factor that has enabled progress in this area in the last decade is the existence of common object sets that have been shared among different research g
Externí odkaz:
http://arxiv.org/abs/2111.01527
In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can
Externí odkaz:
http://arxiv.org/abs/2103.06871
Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consi
Externí odkaz:
http://arxiv.org/abs/2012.07464
Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation. In this work, we propose a generic, compact a
Externí odkaz:
http://arxiv.org/abs/2009.14681
Autor:
Suárez-Hernández, Alejandro, Gaugry, Thierry, Segovia-Aguas, Javier, Bernardin, Antonin, Torras, Carme, Marchal, Maud, Alenyà, Guillem
Learning is usually performed by observing real robot executions. Physics-based simulators are a good alternative for providing highly valuable information while avoiding costly and potentially destructive robot executions. We present a novel approac
Externí odkaz:
http://arxiv.org/abs/2009.08837