Zobrazeno 1 - 10
of 852
pro vyhledávání: '"movement primitives"'
Publikováno v:
Industrial Robot: the international journal of robotics research and application, 2024, Vol. 51, Issue 3, pp. 387-399.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IR-08-2023-0180
Akademický článek
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Publikováno v:
Industrial Robot: the international journal of robotics research and application, 2024, Vol. 51, Issue 2, pp. 326-339.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IR-11-2023-0284
Publikováno v:
IEEE Access, Vol 12, Pp 92598-92611 (2024)
For the development of autonomous robotic systems, Dynamic Movement Primitives (DMP) and Artificial Potential Fields (APF) are two well known techniques. DMPs are a reference algorithm in robotics for one shot learning as they enable learning complex
Externí odkaz:
https://doaj.org/article/50df5a679828467094435a8f6ea7a91f
Publikováno v:
IEEE Access, Vol 12, Pp 44125-44134 (2024)
Compliant physical human-robot interaction (pHRI), as well as the accuracy and robustness of trajectory tracking, are crucial for rehabilitation robots. In this paper, a new sitting/lying lower limb rehabilitation robot, SUT-SLLRR, has been designed
Externí odkaz:
https://doaj.org/article/9ffb8620c5954031bd42e9f3721262cc
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10665 (2024)
Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajec
Externí odkaz:
https://doaj.org/article/77e9048ac56848fb98acb00d030374a9
Publikováno v:
IET Control Theory & Applications, Vol 17, Iss 15, Pp 2056-2063 (2023)
Abstract Autonomous robotics working in the uncertain environment have drawn increasing interests from researchers. Here, an issue of online motion optimization under unknown environment is considered while preserving the safety and improving the fle
Externí odkaz:
https://doaj.org/article/8140429294cf458aae1b7e716e9daad3
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
Traditional trajectory learning methods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the env
Externí odkaz:
https://doaj.org/article/426e9012ab8a40c4a2c4ea3cca4214c5
Publikováno v:
Robotic Intelligence and Automation, 2023, Vol. 43, Issue 2, pp. 85-95.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RIA-07-2022-0199
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4943 (2024)
The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates redemonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acqui
Externí odkaz:
https://doaj.org/article/2f4c4fd749634646a8853ff6578e5539