Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Danny Driess"'
Autor:
Daniel F. B. Haeufle, Isabell Wochner, David Holzmüller, Danny Driess, Michael Günther, Syn Schmitt
Publikováno v:
Frontiers in Robotics and AI, Vol 7 (2020)
It is hypothesized that the nonlinear muscle characteristic of biomechanical systems simplify control in the sense that the information the nervous system has to process is reduced through off-loading computation to the morphological structure. It ha
Externí odkaz:
https://doaj.org/article/ec893cc9ee984fd1b90126b8532af89f
Autor:
Isabell Wochner, Danny Driess, Heiko Zimmermann, Daniel F. B. Haeufle, Marc Toussaint, Syn Schmitt
Publikováno v:
Frontiers in Computational Neuroscience, Vol 14 (2020)
Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many r
Externí odkaz:
https://doaj.org/article/f1c25a84b6784c44b0e345e5fc3f87b3
Publikováno v:
IEEE Robotics and Automation Letters. 7:10857-10864
Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or more general tool-use. To achieve such interactions, traditional approac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ff5934632898dc6e63a76203c93ac54
https://doi.org/10.1109/lra.2022.3194955
https://doi.org/10.1109/lra.2022.3194955
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::876de6644f45ceb90897012ef068a5a4
https://doi.org/10.1109/iros47612.2022.9981758
https://doi.org/10.1109/iros47612.2022.9981758
Publikováno v:
IEEE Robotics and Automation Letters. 5:6209-6216
Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dyn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8ab8b0cc9442d4f50a5587b52c54689
https://doi.org/10.1109/lra.2020.3010462
https://doi.org/10.1109/lra.2020.3010462
In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f04795e9eea7504569de022cdb6fb18
Publikováno v:
ICRA
Long-horizon manipulation tasks require joint reasoning over a sequence of discrete actions and their associated continuous control parameters. While Task and Motion Planning (TAMP) approaches are capable of generating motion plans that account for t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::797ec0a06e163ea32e08b79eceef9477
https://doi.org/10.1109/icra48506.2021.9560934
https://doi.org/10.1109/icra48506.2021.9560934
Publikováno v:
IEEE Transactions on Robotics
Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f2198221894066bd65332303178b4d2
Publikováno v:
ICRA
Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::423f23495861c6a5f38e2c41fff25bd6
Publikováno v:
ICRA
Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d43e40203ab85b727467a2b9e1ead5c
https://doi.org/10.1109/icra40945.2020.9196840
https://doi.org/10.1109/icra40945.2020.9196840