Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Emmanuel Pignat"'
Publikováno v:
IEEE Robotics and Automation Letters
Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9130639cc3d43953376c5df8e9eef8a
https://doi.org/10.1109/lra.2020.2972893
https://doi.org/10.1109/lra.2020.2972893
Publikováno v:
IEEE Robotics and Automation Letters (RA-L)
In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90a424b8b76808499652ab68db79b702
http://arxiv.org/abs/2011.05717
http://arxiv.org/abs/2011.05717
Publikováno v:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
IROS
IROS
Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dbe7100fea628fc5668d3abd6135259
Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g. picking objects
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82be43bd9fe9c90e12620581fabb5a30
Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::777d0994984c89bf287a1c599404e535
Publikováno v:
2020 IEEE International Conference on Robotics and Automation (ICRA)
ICRA
ICRA
We propose to formulate the problem of representing a distribution of robot configurations (e.g. joint angles) as that of approximating a product of experts. Our approach uses variational inference, a popular method in Bayesian computation, which has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78b09922720901fd3a23532a593773b7
Autor:
Sylvain Calinon, Emmanuel Pignat
Publikováno v:
Robotics and Autonomous Systems. 93:61-75
For tasks such as dressing assistance, robots should be able to adapt to different user morphologies, preferences and requirements. We propose a programming by demonstration method to efficiently learn and adapt such skills. Our method encodes sensor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e356eadd03a09070c366bc805249322
https://doi.org/10.1016/j.robot.2017.03.017
https://doi.org/10.1016/j.robot.2017.03.017
Limited time-resolution in microscopy is an obstacle to many biological studies. Despite recent advances in hardware, digital cameras have limited operation modes that constrain frame-rate, integration time, and color sensing patterns. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72d2dcd1ee3aeb79f01eeeec1b09fc50
https://infoscience.epfl.ch/record/270145
https://infoscience.epfl.ch/record/270145
Publikováno v:
ICRA
In this paper we show how different choices regarding compliance affect a dual-arm assembly task. In addition, we present how the compliance parameters can be learned from a human demonstration. Compliant motions can be used in assembly tasks to miti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c215269cf2f08f7a9b1a022c33f330f1
https://infoscience.epfl.ch/record/275410
https://infoscience.epfl.ch/record/275410