Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Wang, Xiaolong"'
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
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we
Externí odkaz:
http://arxiv.org/abs/2405.18418
Scene representations using 3D Gaussian primitives have produced excellent results in modeling the appearance of static and dynamic 3D scenes. Many graphics applications, however, demand the ability to manipulate both the appearance and the physical
Externí odkaz:
http://arxiv.org/abs/2404.01223
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
Autor:
Hu, Yafei, Xie, Quanting, Jain, Vidhi, Francis, Jonathan, Patrikar, Jay, Keetha, Nikhil, Kim, Seungchan, Xie, Yaqi, Zhang, Tianyi, Zhao, Shibo, Chong, Yu Quan, Wang, Chen, Sycara, Katia, Johnson-Roberson, Matthew, Batra, Dhruv, Wang, Xiaolong, Scherer, Sebastian, Kira, Zsolt, Xia, Fei, Bisk, Yonatan
Building general-purpose robots that can operate seamlessly, in any environment, with any object, and utilizing various skills to complete diverse tasks has been a long-standing goal in Artificial Intelligence. Unfortunately, however, most existing r
Externí odkaz:
http://arxiv.org/abs/2312.08782
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
Autor:
Yuan, Ying, Che, Haichuan, Qin, Yuzhe, Huang, Binghao, Yin, Zhao-Heng, Lee, Kang-Won, Wu, Yi, Lim, Soo-Chul, Wang, Xiaolong
Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual feedback. However, the distinct nature of these modalities poses significant challenges. In this paper, we introduce a system that leverages visual and tactile se
Externí odkaz:
http://arxiv.org/abs/2312.01853
TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series of improvements upon the TD-MPC
Externí odkaz:
http://arxiv.org/abs/2310.16828
Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to lea
Externí odkaz:
http://arxiv.org/abs/2310.16029