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About

Viet-Anh Trinh is a researcher at the forefront of robotic manipulation and computer vision, specializing in grasp detection, hand pose estimation, and multimodal fusion. His work addresses critical challenges in enabling robots and interactive systems to perceive and interact with the physical world more naturally and reliably. Trinh’s most cited paper, “Collision-Free Grasp Detection From Color and Depth Images” (2024, 9 citations), tackles the limitations of point cloud data by integrating appearance information, significantly improving grasp generation for robotic tasks. He further advanced the field with “Efficient Multimodal Fusion for Hand Pose Estimation With Hourglass Network” (2024, 6 citations), which enhances real-time hand tracking for applications in VR, AR, and human-robot interaction. Most recently, his “Vote-based multimodal fusion for hand-held object pose estimation” (2025) introduces a novel voting mechanism to fuse RGB and depth data, achieving robust pose estimation for objects held in hand—a key enabler for augmented reality and dexterous manipulation. With a growing citation impact and a focus on practical, real-time solutions, Trinh’s contributions are shaping the next generation of intelligent, perceptive robotic systems.