Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jung-Su Ha"'
Publikováno v:
IEEE Access, Vol 7, Pp 163589-163603 (2019)
This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuri
Externí odkaz:
https://doaj.org/article/7d9206e075fe41389254c2979a116012
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:
International Journal of Systems Science. 52:2228-2240
This work presents a multiscale framework to solve a class of inverse optimal control (IOC) problems in the context of robot motion planning and control in a complex environment. In order to handle...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34f21caf7eae6008cde3bc44e24ff996
https://doi.org/10.1080/00207721.2021.1882011
https://doi.org/10.1080/00207721.2021.1882011
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:
ICRA
Existing work on sequential manipulation planning and trajectory optimization typically assumes the robot, environment and tools to be given. However, in particular in industrial applications, it is highly interesting to ask, what would be an optimal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55c84f97dfc0efd85015391bea2ffb15
https://doi.org/10.1109/icra48506.2021.9561256
https://doi.org/10.1109/icra48506.2021.9561256
Publikováno v:
ICRA
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6fb425e5ef6529ab48f8aa3cd5d920d
https://doi.org/10.1109/icra48506.2021.9561017
https://doi.org/10.1109/icra48506.2021.9561017
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
Publikováno v:
ICRA
In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0843f6d81a6ba441e09c975d518bec93
https://doi.org/10.1109/icra40945.2020.9197291
https://doi.org/10.1109/icra40945.2020.9197291