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of 44
pro vyhledávání: '"Chen, Tianze"'
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on extensive datasets without alignment, and aligned models, deliberatel
Externí odkaz:
http://arxiv.org/abs/2404.10552
A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping. The count plays an important role in determining the robot's next move and the outcome and efficiency of the whole pick-place process. This pape
Externí odkaz:
http://arxiv.org/abs/2404.06631
Autor:
Wang, Xiao, Chen, Tianze, Ge, Qiming, Xia, Han, Bao, Rong, Zheng, Rui, Zhang, Qi, Gui, Tao, Huang, Xuanjing
Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their performance degrades in scenarios where multiple tasks are encountered sequen
Externí odkaz:
http://arxiv.org/abs/2310.14152
Autor:
Wang, Xiao, Zhang, Yuansen, Chen, Tianze, Gao, Songyang, Jin, Senjie, Yang, Xianjun, Xi, Zhiheng, Zheng, Rui, Zou, Yicheng, Gui, Tao, Zhang, Qi, Huang, Xuanjing
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of these aligned LLMs has been largely overlooked. Existing continual learn
Externí odkaz:
http://arxiv.org/abs/2310.06762
Autor:
Wang, Xiao, Zhou, Weikang, Zu, Can, Xia, Han, Chen, Tianze, Zhang, Yuansen, Zheng, Rui, Ye, Junjie, Zhang, Qi, Gui, Tao, Kang, Jihua, Yang, Jingsheng, Li, Siyuan, Du, Chunsai
Large language models have unlocked strong multi-task capabilities from reading instructive prompts. However, recent studies have shown that existing large models still have difficulty with information extraction tasks. For example, gpt-3.5-turbo ach
Externí odkaz:
http://arxiv.org/abs/2304.08085
This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking
Externí odkaz:
http://arxiv.org/abs/2205.15276
Autor:
Chen, Tianze, Su, Shengpeng, Chen, Shuo, Wang, Yizhuang, Huang, Yanfang, Liu, Bingbing, Sun, Hu, Yang, Shuzhen, Han, Guihong
Publikováno v:
In Minerals Engineering February 2025 221
Transferring multiple objects between bins is a common task for many applications. In robotics, a standard approach is to pick up one object and transfer it at a time. However, grasping and picking up multiple objects and transferring them together a
Externí odkaz:
http://arxiv.org/abs/2112.09829
A human hand can grasp a desired number of objects at once from a pile based solely on tactile sensing. To do so, a robot needs to grasp within a pile, sense the number of objects in the grasp before lifting, and predict the number of objects that wi
Externí odkaz:
http://arxiv.org/abs/2112.01270
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