Weiying Wang

Arizona State University, Harvard University

Papers

Total Citations

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About

Weiying Wang is an emerging researcher at the intersection of multi-robot systems, wireless sensing, and simultaneous localization and mapping (SLAM). Their work focuses on leveraging Wi-Fi and wireless signal technologies to solve fundamental challenges in collaborative robotics, particularly around inter-robot communication, pose estimation, and 3D reconstruction. Wang's most recognized contribution, "Active Rendezvous for Multi-robot Pose Graph Optimization Using Sensing over Wi-Fi" (2022, 23 citations), introduced a novel framework enabling robots to actively coordinate their physical meetings using wireless sensing, significantly improving the accuracy of collaborative mapping. Building on this foundation, their Wi-Closure algorithm (2023) addresses the computational bottlenecks inherent in multi-robot loop closure detection by using wireless signals to intelligently prune candidate search spaces, enhancing both efficiency and robustness in large-scale SLAM systems. Most recently, Wang's MULAN-WC framework (2024) represents an ambitious synthesis of wireless coordination with Neural Radiance Fields (NeRF), tackling localization uncertainty in multi-robot 3D reconstruction — a cutting-edge direction that bridges classical robotics with modern neural scene representations. With a growing citation profile and increasingly ambitious research scope, Wang is establishing themselves as a distinctive voice in wireless-assisted collaborative robotics.