Songtao Guo
Papers
5
Total Citations
41
H-Index
5
About
Songtao Guo is a rising researcher at the intersection of robotics, edge computing, and artificial intelligence, whose work is shaping the future of intelligent, distributed robotic systems. His primary research areas include collaborative task offloading, multi-robot systems, and event-driven tactile sensing. Guo’s major contributions lie in developing novel frameworks for optimizing resource allocation and decision-making in complex, heterogeneous networks. For instance, his work on "CollOR" introduces a distributed collaborative offloading and routing mechanism for multi-robot systems, ensuring quality-of-service (QoS) for demanding tasks. Similarly, "SyRoC" pioneers a symbiotic robotics paradigm for IoT-edge-cloud environments, while his game-theoretic approach to robotic offloading addresses the unique challenges of agricultural applications. These contributions have already garnered significant attention, with each of these 2023 papers accumulating over 10 citations. More recently, Guo has ventured into the cutting-edge domain of event-driven tactile sensing, developing a dense spiking graph neural network for high-resolution touch perception, and "EventAugment," a method for learning augmentation policies for asynchronous event-based data. This work is critical for advancing robot manipulation and grasping, showcasing Guo’s ability to tackle fundamental challenges in robotics.
Research Focus
Key Achievements
Top Papers
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- 4Event-Driven Tactile Sensing With Dense Spiking Graph Neural Networks5 citations · 2025
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