Yizhuo Wang — accepted AI-conference papers

2025

CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction

Yizhuo Wang, Haodong He, Jingsong Liang, Yuhong Cao, Ritabrata Chakraborty, Guillaume Adrien Sartoretti

CoRL 2025poster

Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration and point-goal navigation. In both cases, the robot must perceive the environment, update its belief, and accurately estim…

Cited by 0 Source ↗Scholar ↗

2025

MARVEL: Multi-Agent Reinforcement Learning for Constrained Field-of-View Multi-Robot Exploration in Large-Scale Environments

Jimmy Chiun, Shizhe Zhang, Yizhuo Wang, Yuhong Cao, Guillaume Sartoretti

ICRA 2025

In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be th

Cited by 10 Source ↗code ↗Scholar ↗

2024

Deep Reinforcement Learning-Based Large-Scale Robot Exploration

Yuhong Cao, Rui Zhao, Yizhuo Wang, Bairan Xiang, Guillaume Sartoretti

RA-L 2024

In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our DRL-based planner allows the agent to reactively plan its exploration path by making implicit predictions about unknown a

Cited by 43 Source ↗code ↗Scholar ↗

2024

ViPER: Visibility-based Pursuit-Evasion via Reinforcement Learning

Yizhuo Wang, Yuhong Cao, Jimmy Chiun, Subhadeep Koley, Mandy Pham, Guillaume Adrien Sartoretti

CoRL 2024poster

In visibility-based pursuit-evasion tasks, a team of mobile pursuer robots with limited sensing capabilities is tasked with detecting all evaders in a multiply-connected planar environment, whose map may or may not be known to pursuers beforehand. This requires tight coordination among multiple agen…

Cited by 1 Source ↗Scholar ↗

2023

ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration

Yuhong Cao, Tianxiang Hou, Yizhuo Wang, Xian Yi, Guillaume Sartoretti

ICRA 2023poster

In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during exploration, the robot needs to frequently re-plan its path online, as new information is acquired by onboard sensors and…

Cited by 34 Source ↗Scholar ↗

2023

Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets

Yizhuo Wang, Yutong Wang, Yuhong Cao, Guillaume Sartoretti

IROS 2023poster

This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path t…

Cited by 12 Source ↗code ↗Scholar ↗

2022

CAtNIPP: Context-Aware Attention-based Network for Informative Path Planning

Yuhong Cao, Yizhuo Wang, Apoorva Vashisth, Haolin Fan, Guillaume Adrien Sartoretti

CoRL 2022poster

Informative path planning (IPP) is an NP-hard problem, which aims at planning a path allowing an agent to build an accurate belief about a quantity of interest throughout a given search domain, within constraints on resource budget (e.g., path length for robots with limited battery life). IPP requir…

Cited by 32 Source ↗Scholar ↗