Lingxiao Meng

About

Lingxiao Meng is a robotics researcher whose work bridges the gap between human intention and robotic action, focusing on task-oriented manipulation, human-robot interaction, and autonomous service systems. His most impactful contribution, the 2023 paper "Task-Oriented Grasp Prediction with Visual-Language Inputs" (38 citations), introduces a novel framework that enables assistive robots to interpret natural language commands and perform both object grounding and task grounding—selecting the correct tool and grasping it appropriately for household tasks. This work addresses a critical challenge in assistive robotics: making robots understand not just what to pick up, but how to use it. Meng also developed a Virtual Reality-based robot teleoperation system (19 citations) that enhances intuitive control through human-scene interaction, and designed an autonomous multiple-trolley collection system for dynamic airport environments (11 citations), demonstrating real-world deployment of nonholonomic robots. His collaborative fall detection system, integrating Wi-Fi sensing with a mobile companion robot, showcases his commitment to healthcare robotics. With a growing citation record and work spanning from VR teleoperation to autonomous logistics, Meng is advancing the frontier of robots that understand, assist, and collaborate with humans in complex, everyday settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Task-Oriented Grasp Prediction with Visual-Language Inputs

   38 citations · 2023
2. 2
   Virtual Reality Based Robot Teleoperation via Human-Scene Interaction

   19 citations · 2023
3. 3
   Autonomous multiple‐trolley collection system with nonholonomic robots: Design, control, and implementation

   11 citations · 2024
4. 4
   Task-Oriented Grasp Prediction with Visual-Language Inputs

   2 citations · 2023
5. 5
   Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot

   1 citations · 2024

Key Collaborators

Contact & Links