7 papers
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navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps,…
ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics
Jie Chen, Yuxin Cai, Yizhuo Wang +5
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…
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…
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…
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…