Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Chen, Weizhe"'
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
The paper introduces DiSProD, an online planner developed for environments with probabilistic transitions in continuous state and action spaces. DiSProD builds a symbolic graph that captures the distribution of future trajectories, conditioned on a g
Externí odkaz:
http://arxiv.org/abs/2302.01491
Robotic Information Gathering (RIG) relies on the uncertainty of a probabilistic model to identify critical areas for efficient data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However,
Externí odkaz:
http://arxiv.org/abs/2205.06426