Zobrazeno 1 - 10
of 5 941
pro vyhledávání: '"Embodied agent"'
Autor:
Li, Manling, Zhao, Shiyu, Wang, Qineng, Wang, Kangrui, Zhou, Yu, Srivastava, Sanjana, Gokmen, Cem, Lee, Tony, Li, Li Erran, Zhang, Ruohan, Liu, Weiyu, Liang, Percy, Fei-Fei, Li, Mao, Jiayuan, Wu, Jiajun
We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments, we still lack a systematic understanding of their performance becaus
Externí odkaz:
http://arxiv.org/abs/2410.07166
Recent research on instructable agents has used memory-augmented Large Language Models (LLMs) as task planners, a technique that retrieves language-program examples relevant to the input instruction and uses them as in-context examples in the LLM pro
Externí odkaz:
http://arxiv.org/abs/2404.19065
Autor:
Zhao, Zhonghan, Ma, Ke, Chai, Wenhao, Wang, Xuan, Chen, Kewei, Guo, Dongxu, Zhang, Yanting, Wang, Hongwei, Wang, Gaoang
With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output executable actions. Nowadays, Multi-modal Language Models~(MLMs) integrate m
Externí odkaz:
http://arxiv.org/abs/2404.04619
Large language models (LLMs) have recently garnered significant accomplishments in various exploratory tasks, even surpassing the performance of traditional reinforcement learning-based methods that have historically dominated the agent-based field.
Externí odkaz:
http://arxiv.org/abs/2401.17749
Autor:
Zhao, Zhonghan, Chai, Wenhao, Wang, Xuan, Boyi, Li, Hao, Shengyu, Cao, Shidong, Ye, Tian, Wang, Gaoang
Large language models (LLMs) have achieved impressive pro-gress on several open-world tasks. Recently, using LLMs to build embodied agents has been a hotspot. This paper proposes STEVE, a comprehensive and visionary embodied agent in the Minecraft vi
Externí odkaz:
http://arxiv.org/abs/2311.15209
Autor:
Zhai, Shaopeng, Wang, Jie, Zhang, Tianyi, Huang, Fuxian, Zhang, Qi, Zhou, Ming, Hou, Jing, Qiao, Yu, Liu, Yu
Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks. However, e
Externí odkaz:
http://arxiv.org/abs/2401.00006
Autor:
Li, Yuxuan, Weihs, Luca
Pretrained representations from large-scale vision models have boosted the performance of downstream embodied policy learning. We look to understand whether additional self-supervised pretraining on exploration trajectories can build on these general
Externí odkaz:
http://arxiv.org/abs/2312.10069
Natural language serves as the primary mode of communication when an intelligent agent with a physical presence engages with human beings. While a plethora of research focuses on natural language understanding (NLU), encompassing endeavors such as se
Externí odkaz:
http://arxiv.org/abs/2310.15605
Akademický článek
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Autor:
Ye, Weirui, Zhang, Yunsheng, Weng, Haoyang, Gu, Xianfan, Wang, Shengjie, Zhang, Tong, Wang, Mengchen, Abbeel, Pieter, Gao, Yang
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of inter
Externí odkaz:
http://arxiv.org/abs/2310.02635