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of 18
pro vyhledávání: '"Wang, Gaotian"'
Real-world object manipulation has been commonly challenged by physical uncertainties and perception limitations. Being an effective strategy, while caging configuration-based manipulation frameworks have successfully provided robust solutions, they
Externí odkaz:
http://arxiv.org/abs/2410.16481
Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. To date, existing nonprehensile solutions are all robot-centric, i.e., the manipulation actions are generated with robot-relevant intent and their o
Externí odkaz:
http://arxiv.org/abs/2410.00261
Nonprehensile manipulation through precise pushing is an essential skill that has been commonly challenged by perception and physical uncertainties, such as those associated with contacts, object geometries, and physical properties. For this, we prop
Externí odkaz:
http://arxiv.org/abs/2403.13274
Autor:
Qian, Howard H., Lu, Yangxiao, Ren, Kejia, Wang, Gaotian, Khargonkar, Ninad, Xiang, Yu, Hang, Kaiyu
In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen object instance segmentation (UO
Externí odkaz:
http://arxiv.org/abs/2403.01731
The Piecewise Constant Curvature (PCC) model is the most widely used soft robotic modeling and control. However, the PCC fails to accurately describe the deformation of the soft robots when executing dynamic tasks or interacting with the environment.
Externí odkaz:
http://arxiv.org/abs/2203.07929
The 'infinite' passive degrees of freedom of soft robotic arms render their control especially challenging. In this paper, we leverage a previously developed model, which drawing equivalence of the soft arm to a series of universal joints, to design
Externí odkaz:
http://arxiv.org/abs/2201.01480
The compliance of soft robotic arms renders the development of accurate kinematic & dynamical models especially challenging. The most widely used model in soft robotic kinematics assumes Piecewise Constant Curvature (PCC). However, PCC fails to effec
Externí odkaz:
http://arxiv.org/abs/2109.05791
Autor:
Li, Peijin, Wang, Gaotian, Jiang, Hao, Jin, Yusong, Gan, Yinghao, Chen, Xiaoping, Ji, Jianmin
It is challenging to control a soft robot, where reinforcement learning methods have been applied with promising results. However, due to the poor sample efficiency, reinforcement learning methods require a large collection of training data, which li
Externí odkaz:
http://arxiv.org/abs/2109.05795
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