Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Arsalan Mousavian"'
Publikováno v:
The International Journal of Robotics Research. 40:1467-1487
In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac31f889e7841878c43876093c5d38e4
https://doi.org/10.1177/02783649211047634
https://doi.org/10.1177/02783649211047634
Publikováno v:
IEEE Transactions on Robotics. 37:1343-1359
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of larg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d95c6a69fab1b60009371f4bae6158d8
https://doi.org/10.1109/tro.2021.3060341
https://doi.org/10.1109/tro.2021.3060341
Publikováno v:
IEEE Transactions on Robotics. 37:1328-1342
Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao–Blackwellized particle f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7196ce5445241aea4f12d7ed3a5d7372
https://doi.org/10.1109/tro.2021.3056043
https://doi.org/10.1109/tro.2021.3056043
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783030954581
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6a04b555d35b6091407b3c823909ee4
https://doi.org/10.1007/978-3-030-95459-8_55
https://doi.org/10.1007/978-3-030-95459-8_55
Autor:
Jozef van Eenbergen, Hammad Mazhar, Luyang Zhu, Dieter Fox, Arsalan Mousavian, Yu Xiang, Shoubhik Debnath
Publikováno v:
CVPR
Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f42c7354693ede828aa856c17edc245
https://doi.org/10.1109/cvpr46437.2021.00462
https://doi.org/10.1109/cvpr46437.2021.00462
Publikováno v:
ICRA
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0d23e27e1025916c7e0e91550bed797
https://doi.org/10.1109/icra48506.2021.9561877
https://doi.org/10.1109/icra48506.2021.9561877
Publikováno v:
ICRA
Tactile sensing is critical for robotic grasping and manipulation of objects under visual occlusion. However, in contrast to simulations of robot arms and cameras, current simulations of tactile sensors have limited accuracy, speed, and utility. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::301f6b1d9957f3ece7321003764641b3
https://doi.org/10.1109/icra48506.2021.9561969
https://doi.org/10.1109/icra48506.2021.9561969
Publikováno v:
Robotics: Science and Systems
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of objects has been noted to be an important benchmark for AI capabilities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22f8d885118e4408f67ec13df8210921
Publikováno v:
ICRA
Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a7273018a3106cf6b71d9cd180c6998
http://arxiv.org/abs/2011.10726
http://arxiv.org/abs/2011.10726
Publikováno v:
ICRA
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89067db5062f3da9126c4c8d7cce35c1
http://arxiv.org/abs/2011.09584
http://arxiv.org/abs/2011.09584