Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Antonin Raffin"'
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
We propose a fault-tolerant estimation technique for the six-DoF pose of a tendon-driven continuum mechanisms using machine learning. In contrast to previous estimation techniques, no deformation model is required, and the pose prediction is rather p
Externí odkaz:
https://doaj.org/article/16e7bedd20424815a44574e4f21f8b5f
Publikováno v:
ICRA
In contrast to underactuated robotic hands the DLR AWIWI II hand of the David robot is fully controllable because each finger with 4 joints is actuated by 6 or 8 tendons respectively. For such fingers all joint angles (generalized positions) or joint
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f308d94e70c78adb4eaccfdc086cc0f1
https://elib.dlr.de/128252/
https://elib.dlr.de/128252/
Autor:
Antonin Raffin, Ashley Hill, René Traoré, Timothée LESORT, Natalia Díaz-Rodríguez, David Filliat
Publikováno v:
SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning at ICLR 2019
SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning at ICLR 2019, May 2019, Nouvelle Orléans, United States
HAL
SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning at ICLR 2019, May 2019, Nouvelle Orléans, United States
HAL
Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a4047d3e77badae6e3036d244526446
https://hal.archives-ouvertes.fr/hal-02285831
https://hal.archives-ouvertes.fr/hal-02285831
Autor:
Antonin Raffin, Ashley Hill, René Traoré, Timothée LESORT, Natalia Díaz-Rodríguez, David Filliat
Publikováno v:
HAL
NeurIPS 2018 Workshop on “Deep Reinforcement Learning”
NeurIPS 2018 Workshop on “Deep Reinforcement Learning”, Dec 2018, Montreal, Canada
NeurIPS 2018 Workshop on “Deep Reinforcement Learning”
NeurIPS 2018 Workshop on “Deep Reinforcement Learning”, Dec 2018, Montreal, Canada
State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43caccd63e41d619dc3325073e7b659a