Zobrazeno 1 - 10
of 215
pro vyhledávání: '"CHEN Weizhe"'
Autor:
Chen, Weizhe, Zhang, Zhicheng, Liu, Guanlin, Zheng, Renjie, Shi, Wenlei, Dun, Chen, Wu, Zheng, Jin, Xing, Yan, Lin
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. This becomes esp
Externí odkaz:
http://arxiv.org/abs/2410.21236
Current common interactions with language models is through full inference. This approach may not necessarily align with the model's internal knowledge. Studies show discrepancies between prompts and internal representations. Most focus on sentence u
Externí odkaz:
http://arxiv.org/abs/2409.13972
In this past year, large language models (LLMs) have had remarkable success in domains outside the traditional natural language processing, and people are starting to explore the usage of LLMs in more general and close to application domains like cod
Externí odkaz:
http://arxiv.org/abs/2406.11132
Gaussian Process (GP) models are widely used for Robotic Information Gathering (RIG) in exploring unknown environments due to their ability to model complex phenomena with non-parametric flexibility and accurately quantify prediction uncertainty. Pre
Externí odkaz:
http://arxiv.org/abs/2406.03669
Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills some specified requirements is an important task. Previous research in
Externí odkaz:
http://arxiv.org/abs/2404.03554
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centraliz
Externí odkaz:
http://arxiv.org/abs/2404.03101
With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks. However, there i
Externí odkaz:
http://arxiv.org/abs/2401.03630
Autor:
Chen, Weizhe, Liu, Lantao
Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dynamics require the planning module to integrate future environmental c
Externí odkaz:
http://arxiv.org/abs/2306.09608
Autor:
Chen, Weizhe, Liu, Lantao
In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the grow
Externí odkaz:
http://arxiv.org/abs/2306.06578
Publikováno v:
The International Journal of Robotics Research. 2024;43(4):405-436
Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many appli
Externí odkaz:
http://arxiv.org/abs/2306.01263