Erdong Xiao
Papers
6
Total Citations
105
H-Index
3
About
Erdong Xiao is pioneering the frontier of legged and soft robotics, with research spanning proprioceptive sensing for deformable bodies and adaptive locomotion control for quadrupedal robots. His most cited work, "Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing" (51 citations), tackles the long-standing challenge of measuring and modeling high-dimensional 3D shapes of soft bodies without internal sensors—a breakthrough for robotics applications using flexible materials. In legged robotics, Xiao’s "MorAL" framework (25 citations) introduces a learning-based controller that adapts to different quadruped morphologies, moving beyond robot-specific designs to enable versatile locomotion on challenging terrains. His "FT-Net" (22 citations) advances fault-tolerant control, allowing quadrupeds to recover from severe hardware failures during hazardous missions. More recently, Xiao has explored bipedal locomotion for quadrupedal robots, achieving stable skills and automatic fall recovery on uneven ground—a significant step toward more agile and resilient systems. With over 100 total citations and a trajectory of impactful, cross-domain contributions, Xiao is shaping the future of adaptive, resilient robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing51 citations · 2020
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- 5Real-time Soft Body 3D Proprioception via Deep Vision-based Sensing3 citations · 2019
- 6