Zobrazeno 1 - 10
of 220
pro vyhledávání: '"Chen, Zhaopeng"'
Autor:
Bai, Kaixin, Zeng, Huajian, Zhang, Lei, Liu, Yiwen, Xu, Hongli, Chen, Zhaopeng, Zhang, Jianwei
Transparent object depth perception poses a challenge in everyday life and logistics, primarily due to the inability of standard 3D sensors to accurately capture depth on transparent or reflective surfaces. This limitation significantly affects depth
Externí odkaz:
http://arxiv.org/abs/2409.08926
Autor:
Feng, Qian, Lema, David S. Martinez, Malmir, Mohammadhossein, Li, Hang, Feng, Jianxiang, Chen, Zhaopeng, Knoll, Alois
We introduce DexGanGrasp, a dexterous grasping synthesis method that generates and evaluates grasps with single view in real time. DexGanGrasp comprises a Conditional Generative Adversarial Networks (cGANs)-based DexGenerator to generate dexterous gr
Externí odkaz:
http://arxiv.org/abs/2407.17348
Synthesizing diverse and accurate grasps with multi-fingered hands is an important yet challenging task in robotics. Previous efforts focusing on generative modeling have fallen short of precisely capturing the multi-modal, high-dimensional grasp dis
Externí odkaz:
http://arxiv.org/abs/2407.15161
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previous sim2real approaches using domain randomization require extensive s
Externí odkaz:
http://arxiv.org/abs/2407.12449
Autor:
Zhang, Lei, Bai, Kaixin, Huang, Guowen, Bing, Zhenshan, Chen, Zhaopeng, Knoll, Alois, Zhang, Jianwei
The deep learning models has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping. However, the contact information-guided grasping in cluttered environments remains largely underexplored. To address this gap, we
Externí odkaz:
http://arxiv.org/abs/2404.08844
Autor:
Wang, Yunlong, Zhang, Lei, Tu, Yuyang, Zhang, Hui, Bai, Kaixin, Chen, Zhaopeng, Zhang, Jianwei
The exploration of robotic dexterous hands utilizing tools has recently attracted considerable attention. A significant challenge in this field is the precise awareness of a tool's pose when grasped, as occlusion by the hand often degrades the qualit
Externí odkaz:
http://arxiv.org/abs/2404.04193
We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factorin
Externí odkaz:
http://arxiv.org/abs/2402.14498
Traditional visual servoing methods suffer from serving between scenes from multiple perspectives, which humans can complete with visual signals alone. In this paper, we investigated how multi-perspective visual servoing could be solved under robot-s
Externí odkaz:
http://arxiv.org/abs/2312.15809
Dexterous grasping of unseen objects in dynamic environments is an essential prerequisite for the advanced manipulation of autonomous robots. Prior advances rely on several assumptions that simplify the setup, including environment stationarity, pre-
Externí odkaz:
http://arxiv.org/abs/2310.17923
Effect of TiC content on mechanical and tribological properties of TiC/Cu composites prepared by SPS
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 8, Pp 178-188 (2024)
Copper matrix composites reinforced with different mass fractions of TiC particles were prepared by spark plasma sintering （SPS） technology. The microstructures of the copper matrix composites with different sintering temperatures and TiC content
Externí odkaz:
https://doaj.org/article/4a7fcd389a05462582801c9f343877d5
