Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Asali, Ehsan"'
Autor:
Asali, Ehsan, Doshi, Prashant
We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner. Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human
Externí odkaz:
http://arxiv.org/abs/2412.11360
The learn-from-observation (LfO) paradigm is a human-inspired mode for a robot to learn to perform a task simply by watching it being performed. LfO can facilitate robot integration on factory floors by minimizing disruption and reducing tedious prog
Externí odkaz:
http://arxiv.org/abs/2311.08393
Reward engineering and designing an incentive reward function are non-trivial tasks to train agents in complex environments. Furthermore, an inaccurate reward function may lead to a biased behaviour which is far from an efficient and optimised behavi
Externí odkaz:
http://arxiv.org/abs/2105.00499
Autor:
Asali, Ehsan, Shenavarmasouleh, Farzan, Mohammadi, Farid Ghareh, Suresh, Prasanth Sengadu, Arabnia, Hamid R.
For recognizing speakers in video streams, significant research studies have been made to obtain a rich machine learning model by extracting high-level speaker's features such as facial expression, emotion, and gender. However, generating such a mode
Externí odkaz:
http://arxiv.org/abs/2007.06809
In this article, we will discuss methods and ideas which are implemented on Namira 2D Soccer Simulation team in the recent year. Numerous scientific and programming activities were done in the process of code development, but we will mention the most
Externí odkaz:
http://arxiv.org/abs/2006.13534
Publikováno v:
ICRA 2020, pp. 2153-2159
Learning from demonstration (LfD) and imitation learning offer new paradigms for transferring task behavior to robots. A class of methods that enable such online learning require the robot to observe the task being performed and decompose the sensed
Externí odkaz:
http://arxiv.org/abs/1905.04380
Autor:
Asali, Ehsan, Valipour, Mojtaba, Zare, Nader, Afshar, Ardavan, Katebzadeh, MohammadReza, Dastghaibyfard, GH.
Publikováno v:
2016 Artificial Intelligence & Robotics (IRANOPEN); 2016, p140-144, 5p
