Dongdong Hou

Shenyang Institute of Automation, National University of Defense Technology

Papers

Total Citations

108

H-Index

About

Dongdong Hou is a leading researcher in robotic perception and manipulation, with a focus on bridging the gap between autonomous systems and real-world interaction. His work centers on 3-D vision-based robot manipulation, visual-tactile fusion, and open-ended online learning for visual perception. Hou’s 2021 study on 3-D vision for robot manipulation (61 citations) provides a comprehensive framework for applications ranging from industrial manufacturing to medical robotics, significantly advancing pick-and-place and underwater manipulation tasks. In another highly cited work (40 citations), he introduced partial visual-tactile fused learning, a novel approach that overcomes the modality gap between vision and touch to enhance robotic object recognition. Most recently, his 2023 paper on open-ended online learning (7 citations) pushes toward autonomous visual systems that can continuously adapt like human perception, moving beyond static, task-specific models. Hou’s contributions are pivotal for developing more dexterous, perceptive robots capable of operating in unstructured environments, making his research essential reading for students and engineers in robotics and computer vision.