Papers
11
Total Citations
95
H-Index
6
About
Xudong Han is an emerging robotics researcher whose work sits at the intersection of soft robotics, tactile sensing, and embodied intelligence. His research focuses on developing bio-inspired robotic systems that integrate proprioception, tactile perception, and adaptive grasping — particularly in challenging environments such as underwater settings. Drawing inspiration from biological systems like human skin and lobster claws, Han has pioneered the use of soft robotic metamaterials to create structures that blur the boundary between materials and machines, enabling robots to sense and respond to their physical world with remarkable sophistication. Among his most notable contributions is the development of vision-based tactile intelligence using soft robotic metamaterials, which has already garnered 25 citations since its 2024 publication, alongside groundbreaking work on proprioceptive learning with soft polyhedral networks. His research on amphibious tactile sensing and underwater grasping — leveraging autoencoders and high-frame-rate cameras — addresses critical challenges in ocean exploration robotics. With a publication record spanning foundational design principles to AI-driven sensing systems, and accumulating over 90 citations across recent works, Han is rapidly establishing himself as a significant voice in next-generation intelligent soft robotics.
Research Focus
Key Achievements
Top Papers
- 1Vision-based tactile intelligence with soft robotic metamaterial25 citations · 2024
- 2Proprioceptive learning with soft polyhedral networks15 citations · 2024
- 3
- 4Autoencoding a Soft Touch to Learn Grasping from On‐Land to Underwater10 citations · 2023
- 5Proprioceptive State Estimation for Amphibious Tactile Sensing9 citations · 2024
- 6On flange-based 3D hand–eye calibration for soft robotic tactile welding6 citations · 2024
- 7
- 8
- 9Autoencoding a Soft Touch to Learn Grasping from On‐Land to Underwater4 citations · 2024
- 10Reconstructing Soft Robotic Touch via In‐Finger Vision2 citations · 2024