Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Kasaei, Mohammadreza"'
Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions. Despite its potential, the data collection process for IL remains a significant challenge due to the logistical
Externí odkaz:
http://arxiv.org/abs/2407.21244
In this work, we delve into the intricate synergy among non-prehensile actions like pushing, and prehensile actions such as grasping and throwing, within the domain of robotic manipulation. We introduce an innovative approach to learning these synerg
Externí odkaz:
http://arxiv.org/abs/2402.16045
Autor:
Tziafas, Georgios, Xu, Yucheng, Goel, Arushi, Kasaei, Mohammadreza, Li, Zhibin, Kasaei, Hamidreza
Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, which predi
Externí odkaz:
http://arxiv.org/abs/2311.05779
Autor:
Mao, Xiaofeng, Xu, Yucheng, Wen, Ruoshi, Kasaei, Mohammadreza, Yu, Wanming, Psomopoulou, Efi, Lepora, Nathan F., Li, Zhibin
Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of r
Externí odkaz:
http://arxiv.org/abs/2307.04619
Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel \textbf{S}ingle
Externí odkaz:
http://arxiv.org/abs/2302.07824
This work developed a kernel-based residual learning framework for quadrupedal robotic locomotion. Initially, a kernel neural network is trained with data collected from an MPC controller. Alongside a frozen kernel network, a residual controller netw
Externí odkaz:
http://arxiv.org/abs/2302.07343
The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target basket, located outside its feasible kinematic space, without travelin
Externí odkaz:
http://arxiv.org/abs/2210.00609
Autor:
Babarahmati, Keyhan Kouhkiloui, Kasaei, Mohammadreza, Tiseo, Carlo, Mistry, Michael, Vijayakumar, Sethu
In recent years, the need for robots to transition from isolated industrial tasks to shared environments, including human-robot collaboration and teleoperation, has become increasingly evident. Building on the foundation of Fractal Impedance Control
Externí odkaz:
http://arxiv.org/abs/2108.04567
To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, object recognition and grasping are two key functionalities for autonomous
Externí odkaz:
http://arxiv.org/abs/2106.01866
This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connect
Externí odkaz:
http://arxiv.org/abs/2104.10592