Haixu Chi
Papers
1
Total Citations
6
H-Index
1
About
Haixu Chi is a robotics researcher whose work centers on autonomous navigation, obstacle avoidance, and multi-modal perception for real-world robotic systems. His most-cited paper, "DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance" (2023, 6 citations), addresses a critical challenge in safe autonomous interaction: reliable obstacle avoidance despite difficulties in depth perception and latent environmental factors. Chi’s contributions lie in developing learning-based frameworks that integrate visual and ego-motion cues to improve a robot’s ability to navigate complex, dynamic environments. By tackling the interplay between perception and control, his research enhances the robustness of autonomous systems, particularly in scenarios where traditional methods fail. Though early in his career, Chi’s work has already been recognized for its practical implications in field robotics, and his focus on multi-modal learning positions him as an emerging voice in the intersection of computer vision and robot autonomy. His findings offer valuable insights for students and researchers aiming to build safer, more adaptive robots.
Research Focus
Key Achievements
Top Papers
- 1