Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Jens Lundell"'
Publikováno v:
IEEE Transactions on Robotics. 39:41-56
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27b92c8c656d2d0ce0578051f2ddcb9c
https://doi.org/10.1109/tro.2022.3188168
https://doi.org/10.1109/tro.2022.3188168
Publikováno v:
IEEE Robotics and Automation Letters. 7:3038-3045
Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful -- humans are, for example, able to exploit deformations to generate new potentia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b221ba6d5ed1b5ccb157e58ee43f124f
https://doi.org/10.1109/lra.2022.3146551
https://doi.org/10.1109/lra.2022.3146551
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Evaluation of grasps on deformable 3D objects is a little-studied problem, even if the applicability of rigid object grasp quality measures for deformable ones is an open question. A central issue with most quality measures is their dependence on con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1ffb0bd21261ff14736109fbe1ba3c5
https://doi.org/10.1109/iros47612.2022.9981169
https://doi.org/10.1109/iros47612.2022.9981169
Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c9cda02f4627cb363953bb2bd7f228b
Publikováno v:
Aalto University
Grasping deformable objects is not well researched due to the complexity in modelling and simulating the dynamic behavior of such objects. However, with the rapid development of physics-based simulators that support soft bodies, the research gap betw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc6e7ce46f61f0ea3252756cc7c40e14
http://arxiv.org/abs/2107.08898
http://arxiv.org/abs/2107.08898
Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a5eb882aea7caa9edbe16164fa80b75
http://arxiv.org/abs/2103.04783
http://arxiv.org/abs/2103.04783
Autor:
Grégory Rogez, Jens Lundell, Tran Nguyen Le, Ville Kyrki, Francesco Verdoja, Enric Corona, Philippe Weinzaepfel, Francesc Moreno-Noguer
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
ICRA
Digital.CSIC. Repositorio Institucional del CSIC
instname
Universitat Politècnica de Catalunya (UPC)
ICRA
Digital.CSIC. Repositorio Institucional del CSIC
instname
Trabajo presentado en el International Conference on Robotics and Automation (ICRA), celebrado de forma híbrida (virtual y presencial), desde Xian (China), del 30 de mayo al 5 de junio de 2021
While there exists many methods for manipulating ri
While there exists many methods for manipulating ri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16308d02aeddc0a27b65fd4eb43e44ae
http://arxiv.org/abs/2012.09696
http://arxiv.org/abs/2012.09696
Publikováno v:
ICRA
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0077daa9e9deda2c8a55c2940d5c9237
http://arxiv.org/abs/1909.12908
http://arxiv.org/abs/1909.12908
Publikováno v:
IROS
We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object as input and outputs the completed shape as a voxel grid. The key part
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce5dfc26a15248e2a539b89921df26df
http://arxiv.org/abs/1903.00645
http://arxiv.org/abs/1903.00645
Publikováno v:
SMC
Safe yet stable grasping requires a robotic hand to apply sufficient force on the object to immobilize it while keeping it from getting damaged. Soft robotic hands have been proposed for safe grasping due to their passive compliance, but even such a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c6da4e21cdee1c793f6ac096497e0fb