Zobrazeno 1 - 5
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pro vyhledávání: '"En Yen Puang"'
In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data. The proposed SA-CNN introduces convolution and transposed convolution stacks to c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99bb83987126cdd56c36376f6bbfb59e
Publikováno v:
IROS
We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model cou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c517cbb9f4c9e1d583a66adbfdce0e5
http://arxiv.org/abs/2007.13960
http://arxiv.org/abs/2007.13960
Autor:
Diego Rodriguez, Max Schwarz, Arul Selvam Periyasamy, Sven Behnke, David Droeschel, En Yen Puang, Sebastian Schüller, Jan Razlaw, Michael Schreiber, Christian Lenz
Publikováno v:
Journal of Field Robotics. 36:170-182
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state-of-the-art in autonomous operation of ground-based and flying robots. In this article, we describe our winning entry to MBZ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd84effc6ac11c39fa080c103ba262a3
https://doi.org/10.1002/rob.21826
https://doi.org/10.1002/rob.21826
Autor:
En Yen Puang, Maximilian Durner, Rudolph Triebel, Zoltan-Csaba Marton, Peter Lehner, Alin Albu-Schaffer
Publikováno v:
ICRA
One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a45850f7052a364ace03aa6ce3d8083
https://elib.dlr.de/128182/
https://elib.dlr.de/128182/
Autor:
Zoltan-Csaba Marton, Narunas Vaskevicius, Martin Sundermeyer, En Yen Puang, Maximilian Durner, Rudolph Triebel, Kai O. Arras
Publikováno v:
CVPR
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9fc504c110a16827e1d2ea523cf141d