Ran Long
Papers
2
Total Citations
69
H-Index
2
About
Ran Long is at the forefront of embodied AI and robotic intelligence, pioneering how machines perceive and act within unpredictable, real-world environments. His most impactful work, "Embodied large language models enable robots to complete complex tasks in unpredictable environments," has already garnered 67 citations, reflecting its significance in bridging large language models with sensorimotor control—a biologically inspired approach that promises a step change in robotic autonomy. Long’s research addresses a critical bottleneck: enabling robots to reason and adapt on the fly, rather than relying on rigid, pre-programmed routines. Earlier, in "RigidFusion: Robot Localisation and Mapping in Environments With Large Dynamic Rigid Objects" (2021), he tackled a fundamental challenge in SLAM—handling large, moving objects that occlude the camera view. While less cited, this work laid essential groundwork for robust perception in cluttered, dynamic settings. By combining cutting-edge AI with classical robotics, Long is shaping a future where machines operate seamlessly alongside humans, from manufacturing floors to disaster response. His contributions are vital for students and researchers exploring the intersection of language, vision, and action.
Research Focus
Key Achievements
Top Papers
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- 2