4 papers
RadarEye: Robust Liquid Level Tracking Using mmWave Radar in Robotic Pouring
Hongyu Deng, He Chen
Transparent liquid manipulation in robotic pouring remains challenging for perception systems: specular/refraction effects and lighting variability degrade visual cues, undermining…
Can Large Language Models Identify Materials from Radar Signals?
Jiangyou Zhu, Hongyu Deng, He Chen
Accurately identifying the material composition of objects is a critical capability for AI robots powered by large language models (LLMs) to perform context-aware manipulation. Rad…
Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference
Zexiang Guo, Hengxiang Chen, Xinheng Mai +5
Inferring physical properties can significantly enhance robotic manipulation by enabling robots to handle objects safely and efficiently through adaptive grasping strategies. Previ…
FuseGrasp: Radar-Camera Fusion for Robotic Grasping of Transparent Objects
Hongyu Deng, Tianfan Xue, He Chen
Transparent objects are prevalent in everyday environments, but their distinct physical properties pose significant challenges for camera-guided robotic arms. Current research is m…