Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Yang, Ruihan"'
Autor:
Ding, Runyu, Qin, Yuzhe, Zhu, Jiyue, Jia, Chengzhe, Yang, Shiqi, Yang, Ruihan, Qi, Xiaojuan, Wang, Xiaolong
Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots with bimanual dexterous hands remains a challenge. Existing teleoperation systems struggle to handle the complexity of coordinating two hands for intricate ma
Externí odkaz:
http://arxiv.org/abs/2407.03162
Autor:
Yang, Ruihan, Chen, Jiangjie, Zhang, Yikai, Yuan, Siyu, Chen, Aili, Richardson, Kyle, Xiao, Yanghua, Yang, Deqing
Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level goals without detailed instructi
Externí odkaz:
http://arxiv.org/abs/2406.04784
Autor:
Cheng, An-Chieh, Yin, Hongxu, Fu, Yang, Guo, Qiushan, Yang, Ruihan, Kautz, Jan, Wang, Xiaolong, Liu, Sifei
Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhanc
Externí odkaz:
http://arxiv.org/abs/2406.01584
Constructing fast samplers for unconditional diffusion and flow-matching models has received much attention recently; however, existing methods for solving inverse problems, such as super-resolution, inpainting, or deblurring, still require hundreds
Externí odkaz:
http://arxiv.org/abs/2405.17673
Autor:
Chen, Jiangjie, Wang, Xintao, Xu, Rui, Yuan, Siyu, Zhang, Yikai, Shi, Wei, Xie, Jian, Li, Shuang, Yang, Ruihan, Zhu, Tinghui, Chen, Aili, Li, Nianqi, Chen, Lida, Hu, Caiyu, Wu, Siye, Ren, Scott, Fu, Ziquan, Xiao, Yanghua
Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate assigned personas. By harnessing multiple advanced abilities of LLMs, i
Externí odkaz:
http://arxiv.org/abs/2404.18231
Autor:
Liu, Minghuan, Chen, Zixuan, Cheng, Xuxin, Ji, Yandong, Qiu, Ri-Zhao, Yang, Ruihan, Wang, Xiaolong
We study the problem of mobile manipulation using legged robots equipped with an arm, namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an opportunity to amplify the manipulation capabilities by conducting wh
Externí odkaz:
http://arxiv.org/abs/2403.16967
Autor:
Qiu, Ri-Zhao, Hu, Yafei, Yang, Ge, Song, Yuchen, Fu, Yang, Ye, Jianglong, Mu, Jiteng, Yang, Ruihan, Atanasov, Nikolay, Scherer, Sebastian, Wang, Xiaolong
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while under
Externí odkaz:
http://arxiv.org/abs/2403.07563
Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a policy, we leve
Externí odkaz:
http://arxiv.org/abs/2402.16796
Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty. Decoding-based watermark, particularly the GumbelMax-trick-based watermark(GM watermark), is a standout solu
Externí odkaz:
http://arxiv.org/abs/2402.12948
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tasks requiring coordinated behaviors such as opening doors. The factoriz
Externí odkaz:
http://arxiv.org/abs/2312.06639