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20212025
most citedPareto Monte Carlo Tree Search for Multi-Objective Informative Planning

50 citations · 51 across the 2 of their papers we have counts for

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7 papers · 1 filter

cs.RO2025

Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space

Durgakant Pushp, Weizhe Chen, Zheng Chen +3

Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings,…

cs.RO2024

POAM: Probabilistic Online Attentive Mapping for Efficient Robotic Information Gathering

Weizhe Chen, Lantao Liu, Roni Khardon

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-para…

cs.RO2023

Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning

Weizhe Chen, Lantao Liu

Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dy…

cs.RO2023

Long-Term Autonomous Ocean Monitoring with Streaming Samples

Weizhe Chen, Lantao Liu

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussi…

cs.RO2023

Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes

Weizhe Chen, Roni Khardon, Lantao Liu

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…

cs.RO20221 cited

AK: Attentive Kernel for Information Gathering

Weizhe Chen, Roni Khardon, Lantao Liu

Robotic Information Gathering (RIG) relies on the uncertainty of a probabilistic model to identify critical areas for efficient data collection. Gaussian processes (GPs) with stati…