1 code implementation • 24 Sep 2019 • Pooya Abolghasemi, Ladislau Bölöni
In addition, we find that both ASOR-IA and ASOR-EA outperform previous approaches even in uncluttered environments, with ASOR-EA performing better even in clutter compared to the previous best baseline in an uncluttered environment.
1 code implementation • CVPR 2019 • Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah, Ladislau Boloni
In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA).
no code implementations • 26 Sep 2018 • Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah, Ladislau Bölöni
In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA).
1 code implementation • 10 Jul 2017 • Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine
We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation.
no code implementations • 12 Mar 2016 • Rouhollah Rahmatizadeh, Pooya Abolghasemi, Aman Behal, Ladislau Bölöni
Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes.