Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Hu, Mengkang"'
Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate executable actions for target tasks. The effectiveness of these agents
Externí odkaz:
http://arxiv.org/abs/2408.09559
Autor:
Hu, Mengkang, Zhao, Pu, Xu, Can, Sun, Qingfeng, Lou, Jianguang, Lin, Qingwei, Luo, Ping, Rajmohan, Saravan, Zhang, Dongmei
Large Language Model (LLM) based agents have garnered significant attention and are becoming increasingly popular. Furthermore, planning ability is a crucial component of an LLM-based agent, involving interaction with the environment and executing ac
Externí odkaz:
http://arxiv.org/abs/2408.00764
Dual-arm robots offer enhanced versatility and efficiency over single-arm counterparts by enabling concurrent manipulation of multiple objects or cooperative execution of tasks using both arms. However, effectively coordinating the two arms for compl
Externí odkaz:
http://arxiv.org/abs/2406.09953
Autor:
Wang, Weiyun, Zhang, Shuibo, Ren, Yiming, Duan, Yuchen, Li, Tiantong, Liu, Shuo, Hu, Mengkang, Chen, Zhe, Zhang, Kaipeng, Lu, Lewei, Zhu, Xizhou, Luo, Ping, Qiao, Yu, Dai, Jifeng, Shao, Wenqi, Wang, Wenhai
With the rapid advancement of multimodal large language models (MLLMs), their evaluation has become increasingly comprehensive. However, understanding long multimodal content, as a foundational ability for real-world applications, remains underexplor
Externí odkaz:
http://arxiv.org/abs/2406.07230
Autor:
Lai, Yao, Lee, Sungyoung, Chen, Guojin, Poddar, Souradip, Hu, Mengkang, Pan, David Z., Luo, Ping
Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital
Externí odkaz:
http://arxiv.org/abs/2405.14918
Due to the concise and structured nature of tables, the knowledge contained therein may be incomplete or missing, posing a significant challenge for table question answering (TableQA) and data analysis systems. Most existing datasets either fail to a
Externí odkaz:
http://arxiv.org/abs/2405.08099
Autor:
Mu, Yao, Chen, Junting, Zhang, Qinglong, Chen, Shoufa, Yu, Qiaojun, Ge, Chongjian, Chen, Runjian, Liang, Zhixuan, Hu, Mengkang, Tao, Chaofan, Sun, Peize, Yu, Haibao, Yang, Chao, Shao, Wenqi, Wang, Wenhai, Dai, Jifeng, Qiao, Yu, Ding, Mingyu, Luo, Ping
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successes in applying multimodal large language models for high-level understa
Externí odkaz:
http://arxiv.org/abs/2402.16117
Autor:
Hu, Mengkang, Mu, Yao, Yu, Xinmiao, Ding, Mingyu, Wu, Shiguang, Shao, Wenqi, Chen, Qiguang, Wang, Bin, Qiao, Yu, Luo, Ping
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2310.08582
Autor:
Mu, Yao, Zhang, Qinglong, Hu, Mengkang, Wang, Wenhai, Ding, Mingyu, Jin, Jun, Wang, Bin, Dai, Jifeng, Qiao, Yu, Luo, Ping
Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model
Externí odkaz:
http://arxiv.org/abs/2305.15021
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks. However, auto-re
Externí odkaz:
http://arxiv.org/abs/2205.12682