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pro vyhledávání: '"Enayati, Amir M. Soufi"'
Multi-object tracking (MOT) is a prominent task in computer vision with application in autonomous driving, responsible for the simultaneous tracking of multiple object trajectories. Detection-based multi-object tracking (DBT) algorithms detect object
Externí odkaz:
http://arxiv.org/abs/2404.03110
This paper addresses the challenge of geometric quality assurance in manufacturing, particularly when human assessment is required. It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning (ML) mode
Externí odkaz:
http://arxiv.org/abs/2405.14877
Autor:
Karpichev, Yehor, Charter, Todd, Hong, Jayden, Enayati, Amir M. Soufi, Honari, Homayoun, Tamizi, Mehran Ghafarian, Najjaran, Homayoun
The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization. Human-ro
Externí odkaz:
http://arxiv.org/abs/2403.14597
Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like skills to robots by Learning from Demonstrations (LfD). The human demon
Externí odkaz:
http://arxiv.org/abs/2304.05703
Reinforcement learning demonstrates significant potential in automatically building control policies in numerous domains, but shows low efficiency when applied to robot manipulation tasks due to the curse of dimensionality. To facilitate the learning
Externí odkaz:
http://arxiv.org/abs/2304.06055
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally, RL agents are sensitive to the discrepancies between the simulation a
Externí odkaz:
http://arxiv.org/abs/2304.06056
Publikováno v:
IEEE Transactions on Robotics; 2024, Vol. 40 Issue: 1 p4733-4749, 17p
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally, RL agents are sensitive to the discrepancies between the simulation a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c93fa6d5b380d937a58e07dbaf6cf56a
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