Zobrazeno 1 - 10
of 369
pro vyhledávání: '"ZHAO Xufeng"'
Publikováno v:
电力工程技术, Vol 43, Iss 3, Pp 192-200 (2024)
Accurately predicting the concentration trend of dissolved gas in oil has a positive effect on the evaluation of transformer status and life assessment. In order to improve the accuracy of dissolved gas in oil prediction, a dissolved gas in oil predi
Externí odkaz:
https://doaj.org/article/9e1752c2d35b41129988efe12042cfdd
Publikováno v:
Kongzhi Yu Xinxi Jishu, Iss 1, Pp 33-39 (2022)
To solve the problems such as braking energy of pure diesel locomotive can only be wasted and pure battery locomotives have low traction power and short cruising range, bidirectional DC/DC circuit of traction battery is integrated into the traction c
Externí odkaz:
https://doaj.org/article/e3c5b95ef7294c37b1dfaab654c783d9
Aligning large language models (LLMs) to human preferences is a crucial step in building helpful and safe AI tools, which usually involve training on supervised datasets. Popular algorithms such as Direct Preference Optimization rely on pairs of AI-g
Externí odkaz:
http://arxiv.org/abs/2409.17169
Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically can potentially express any probable distribution over tokens, it remain
Externí odkaz:
http://arxiv.org/abs/2406.18505
The state of an object reflects its current status or condition and is important for a robot's task planning and manipulation. However, detecting an object's state and generating a state-sensitive plan for robots is challenging. Recently, pre-trained
Externí odkaz:
http://arxiv.org/abs/2406.09988
Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. Existing approaches either m
Externí odkaz:
http://arxiv.org/abs/2405.15019
Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks involving two hands is still challenging because of the difficulties in eff
Externí odkaz:
http://arxiv.org/abs/2404.02018
Autor:
Lu, Wenhao, Zhao, Xufeng, Fryen, Thilo, Lee, Jae Hee, Li, Mengdi, Magg, Sven, Wermter, Stefan
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack of transparency in RL models has been a long-standing problem, making
Externí odkaz:
http://arxiv.org/abs/2401.00104
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Reinforcement Learning (RL) plays an important role in the robotic manipulation domain since it allows self-learning from trial-and-error interactions with the environment. Still, sample efficiency and reward specification seriously limit its potenti
Externí odkaz:
http://arxiv.org/abs/2311.02379
Autor:
Zhao, Xufeng, Li, Mengdi, Lu, Wenhao, Weber, Cornelius, Lee, Jae Hee, Chu, Kun, Wermter, Stefan
Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring
Externí odkaz:
http://arxiv.org/abs/2309.13339