154 citations · 158 across the 11 of their papers we have counts for
11 papers
ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics
Jie Chen, Yuxin Cai, Yizhuo Wang +6
Enabling robots to navigate open-world environments via natural language is critical for general-purpose autonomy. Yet, Vision-Language Navigation has relied on end-to-end policies…
ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation
Shizhe Zhang, Jingsong Liang, Zhitao Zhou +6
Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps,…
HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward
Yuhong Cao, Yizhuo Wang, Jingsong Liang +4
This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. We present HEADER, an attentio…
CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction
Yizhuo Wang, Haodong He, Jingsong Liang +3
Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration an…
COMPASS: Cooperative Multi-Agent Persistent Monitoring using Spatio-Temporal Attention Network
Xingjian Zhang, Yizhuo Wang, Guillaume Sartoretti
Persistent monitoring of dynamic targets is essential in real-world applications such as disaster response, environmental sensing, and wildlife conservation, where mobile agents mu…
Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao +2
In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot…
MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments
Jimmy Chiun, Shizhe Zhang, Yizhuo Wang +2
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 L…
Deep Reinforcement Learning-based Large-scale Robot Exploration
Yuhong Cao, Rui Zhao, Yizhuo Wang +2
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 D…
Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets
Yizhuo Wang, Yutong Wang, Yuhong Cao +1
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 sensi…
ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration
Yuhong Cao, Tianxiang Hou, Yizhuo Wang +2
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 e…
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Renhe Jiang, Du Yin, Zhaonan Wang +7
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car…