Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Guo, Yijie"'
Autor:
Duan, Yiqun, Zhou, Jinzhao, Jiang, Xiaowei, Zhang, Qiang, Sun, Jingkai, Cao, Jiahang, Wang, Jiaxu, Yang, Yiqian, Zhao, Wen, Han, Gang, Guo, Yijie, Lin, Chin-Teng
Recent advancements in humanoid robotics, including the integration of hierarchical reinforcement learning-based control and the utilization of LLM planning, have significantly enhanced the ability of robots to perform complex tasks. In contrast to t
Externí odkaz:
http://arxiv.org/abs/2410.02141
Autor:
Duan, Jiafei, Pumacay, Wilbert, Kumar, Nishanth, Wang, Yi Ru, Tian, Shulin, Yuan, Wentao, Krishna, Ranjay, Fox, Dieter, Mandlekar, Ajay, Guo, Yijie
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models (VLMs) and large language models (LLMs) have improved robots' spatial
Externí odkaz:
http://arxiv.org/abs/2410.00371
Autor:
Cao, Jiahang, Zhang, Qiang, Sun, Jingkai, Wang, Jiaxu, Cheng, Hao, Li, Yulin, Ma, Jun, Shao, Yecheng, Zhao, Wen, Han, Gang, Guo, Yijie, Xu, Renjing
Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typically rely on large parameter U
Externí odkaz:
http://arxiv.org/abs/2409.07163
Autor:
Guo, Yijie, Huang, Zhenhan, Wang, Ruhan, Yao, Zhihao, Yu, Tianyu, Xu, Zhiling, Zhao, Xinyu, Li, Xueqing, Mi, Haipeng
While Swarm User Interfaces (SUIs) have succeeded in enriching tangible interaction experiences, their limitations in autonomous action planning have hindered the potential for personalized and dynamic interaction generation in tabletop games. Based
Externí odkaz:
http://arxiv.org/abs/2407.17086
In this work, we study how to build a robotic system that can solve multiple 3D manipulation tasks given language instructions. To be useful in industrial and household domains, such a system should be capable of learning new tasks with few demonstra
Externí odkaz:
http://arxiv.org/abs/2406.08545
Autor:
Cao, Jiahang, Zhang, Qiang, Wang, Ziqing, Sun, Jingkai, Wang, Jiaxu, Cheng, Hao, Shao, Yecheng, Zhao, Wen, Han, Gang, Guo, Yijie, Xu, Renjing
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, achieving significant success. However, RL trajectories possess unique
Externí odkaz:
http://arxiv.org/abs/2406.02013
The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to predict human joint coordinates in 3D space. Despite recent advancements in deep learning-based methods, they mostly ignore the capability of coupling accessible texts and natural
Externí odkaz:
http://arxiv.org/abs/2405.05216
Autor:
Van Wyk, Karl, Handa, Ankur, Makoviychuk, Viktor, Guo, Yijie, Allshire, Arthur, Ratliff, Nathan D.
Robotics policies are always subjected to complex, second order dynamics that entangle their actions with resulting states. In reinforcement learning (RL) contexts, policies have the burden of deciphering these complicated interactions over massive a
Externí odkaz:
http://arxiv.org/abs/2405.02250
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning. The ef
Externí odkaz:
http://arxiv.org/abs/2404.07428
Autor:
Wang, Jiaxu, Zhang, Qiang, Sun, Jingkai, Cao, Jiahang, Han, Gang, Zhao, Wen, Zhang, Weining, Shao, Yecheng, Guo, Yijie, Xu, Renjing
An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment representation directly influences the achievement of the learning task. Previ
Externí odkaz:
http://arxiv.org/abs/2404.07950