Runqing Miao

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Runqing Miao is a researcher advancing the frontier of autonomous robotics, with a focus on enabling robots to understand and execute complex, long-term manipulation tasks in human environments. Their work centers on integrating semantic knowledge and structured representations—such as scene graphs and knowledge graphs—to bridge the gap between human intent and robotic action. In their highly cited 2023 paper, “Long-term robot manipulation task planning with scene graph and semantic knowledge” (22 citations), Miao developed a framework that allows robots to parse human-described tasks and plan actions across diverse scenes, from daily chores to industrial assembly. This contribution addresses a critical challenge in task and motion planning by embedding semantic context into the planning pipeline. A second influential work, “Semantic Representation of Robot Manipulation with Knowledge Graph” (6 citations), further explores how factors like scenes, objects, and actions can be formally represented to align robotic reasoning with human cognition. Miao’s research is pivotal for creating service robots that can operate autonomously and intuitively in unstructured settings, making their work essential reading for those interested in knowledge-driven robotics and human-robot interaction.