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About

Khanh-Toan Phan is a computer vision and robotics researcher whose work sits at the intersection of 3D perception, pose estimation, and autonomous robotic manipulation. His research consistently addresses some of the most demanding challenges in enabling machines to understand and interact with the physical world, spanning object pose estimation, grasp detection, and hand-object configuration recovery. Among his most recognized contributions is his work on graspability-aware object pose estimation in cluttered scenes (2024, 18 citations), which advances how robots recognize and interact with objects during real-world manipulation tasks. His parallel investigations into grasp configuration synthesis using 3D point clouds and attention mechanisms (2023, 17 citations) demonstrate a sustained commitment to making robotic grasping more robust and intelligent. Phan has also made notable strides in multi-modal fusion approaches, developing adaptive architectures that combine RGB and depth data for more accurate hand and object pose recovery — work that holds direct relevance for augmented reality, virtual reality, and imitation-based robot learning. With over 60 cumulative citations across recent publications, Phan's growing body of work reflects both strong academic impact and practical significance. His research offers meaningful tools for researchers and engineers working on next-generation human-robot interaction systems.