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pro vyhledávání: '"BOWMAN, MICHAEL"'
Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper
Externí odkaz:
http://arxiv.org/abs/2210.16423
In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and the complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning based approaches mainly focus on
Externí odkaz:
http://arxiv.org/abs/2210.05767
Learning-based grasping can afford real-time grasp motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, learning-based methods are required to explore large search spaces during the learning process.
Externí odkaz:
http://arxiv.org/abs/2205.13561
Autor:
Bowman, Michael mike.bowman@outlook.com
Publikováno v:
Review of Optometric Business. 7/31/2024, p1-4. 4p.
Autor:
Caddell, Richard
Publikováno v:
Journal of Environmental Law, 2012 Jan 01. 24(1), 173-175.
Externí odkaz:
https://www.jstor.org/stable/26168420
Autor:
Fallas, John
Publikováno v:
Tempo, 2009 Jul 01. 63(249), 87-89.
Externí odkaz:
https://www.jstor.org/stable/40496117
Autor:
Brown, Silas, Marchi, Sydney, Thomas, C. Sumner, Hale, Ashlyn R., Lockart, Molly, Bowman, Michael K., Christou, George, Woski, Stephen A., Vincent, John B.
Publikováno v:
In Journal of Inorganic Biochemistry February 2024 251
Publikováno v:
in IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 762-769, April 2022
In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world environment
Externí odkaz:
http://arxiv.org/abs/2012.10773