Zobrazeno 1 - 10
of 70
pro vyhledávání: '"DEHGHAN, Masood"'
Autor:
Przystupa, Michael, Johnstonbaugh, Kerrick, Zhang, Zichen, Petrich, Laura, Dehghan, Masood, Haghverd, Faezeh, Jagersand, Martin
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 857-863
Identifying an appropriate task space that simplifies control solutions is important for solving robotic manipulation problems. One approach to this problem is learning an appropriate low-dimensional action space. Linear and nonlinear action mapping
Externí odkaz:
http://arxiv.org/abs/2410.21441
Autor:
Zhang, Zichen, Kirschner, Johannes, Zhang, Junxi, Zanini, Francesco, Ayoub, Alex, Dehghan, Masood, Schuurmans, Dale
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve continuous-time systems where the time discretization, in principle, ca
Externí odkaz:
http://arxiv.org/abs/2212.08949
Designing adaptable control laws that can transfer between different robots is a challenge because of kinematic and dynamic differences, as well as in scenarios where external sensors are used. In this work, we empirically investigate a neural networ
Externí odkaz:
http://arxiv.org/abs/2106.06083
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers focus on assistive tasks from the perspective of various control schemes and mo
Externí odkaz:
http://arxiv.org/abs/2104.03892
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present bottom up solutions using various control methods for different types o
Externí odkaz:
http://arxiv.org/abs/2101.02750
Autor:
Qin, Xuebin, Zhang, Zichen, Huang, Chenyang, Dehghan, Masood, Zaiane, Osmar R., Jagersand, Martin
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD). The architecture of our U$^2$-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to c
Externí odkaz:
http://arxiv.org/abs/2005.09007
We consider the problem of visual imitation learning without human supervision (e.g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL) training environment. We present a geometric perspective to derive s
Externí odkaz:
http://arxiv.org/abs/2003.02768
Manipulation tasks in daily life, such as pouring water, unfold intentionally under specialized manipulation contexts. Being able to process contextual knowledge in these Activities of Daily Living (ADLs) over time can help us understand manipulation
Externí odkaz:
http://arxiv.org/abs/2003.01163
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work stems from the observation that humans perform eye-hand coordination t
Externí odkaz:
http://arxiv.org/abs/1911.04418