Papers
9
Total Citations
116
H-Index
6
About
Chi-Wing Fu is a leading researcher in robotics and computer vision, with a primary focus on bridging the sim-to-real gap for industrial automation. His major contributions lie in developing robust, learning-based frameworks for robotic bin picking and packing—critical tasks in manufacturing and logistics. Fu’s S2R-Pick framework (52 citations) pioneered a generic deep-learning approach for object recognition and localization, enabling robots to reliably handle the textured, varied objects typical of industrial environments. He further advanced the field with SDF-Pack and PPN-Pack, which use signed-distance fields and proposal networks, respectively, to achieve compact, efficient bin packing. Beyond industrial robotics, Fu has explored human-robot interaction through the innovative concept of placing domestic robots as “buddies” on social media contact lists, a line of work that has garnered sustained interest (19 and 9 citations). His recent work on uncertainty-aware suction grasping and embodiment-agnostic action planning continues to push boundaries, addressing real-world challenges like noisy sensor data and generalizability across robot morphologies. With a strong publication record and growing citation impact, Chi-Wing Fu is shaping the future of practical, deployable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Accurate Grid Keypoint Learning for Efficient Video Prediction11 citations · 2021
- 4
- 5Robots in my contact list9 citations · 2012
- 6Uncertainty-Aware Suction Grasping for Cluttered Scenes7 citations · 2024
- 7PPN-Pack: Placement Proposal Network for Efficient Robotic Bin Packing6 citations · 2024
- 8
- 9Embodiment-agnostic Action Planning via Object-Part Scene Flow1 citations · 2025