Yupeng Zhang
Papers
1
Total Citations
2
H-Index
1
About
Yupeng Zhang is a rising researcher at the intersection of robotics, computer vision, and self‑modeling systems. His most‑cited work, “Leveraging Part‑Based NeRF for Robot Self‑Modeling and Control” (2025, 2 citations), introduces a novel framework that uses Neural Radiance Fields (NeRF) to enable robots to autonomously learn their own morphology and kinematics from visual data. This approach reduces the need for manual calibration and adapts to changes in the robot’s body over time—a critical step toward truly autonomous systems. Zhang’s contributions lie in bridging 3D scene representation with robotic control, allowing machines to build internal models of themselves without prior knowledge. While still early in his career, his work has already garnered attention for its potential to transform robot self‑awareness and adaptive control in dynamic environments. By combining part‑based NeRF with real‑time control, Zhang is paving the way for more resilient, self‑improving robots capable of operating in unstructured settings—a key challenge in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Leveraging Part‐Based NeRF for Robot Self‐Modeling and Control2 citations · 2025