Leiliang Gong
Papers
2
Total Citations
9
H-Index
1
About
Leiliang Gong is a rising researcher at the intersection of human-robot interaction and extended reality, whose work focuses on making collaborative robots (cobots) both safer and more intuitive for human partners. His most-cited paper, "A Complementary Framework for Human–Robot Collaboration With a Mixed AR–Haptic Interface" (2023, 8 citations), directly tackles the fundamental tradeoff between safety and efficiency in human-robot collaboration. By integrating augmented reality with haptic feedback, Gong’s framework enables robots to adapt to dynamic tasks without sacrificing speed—a critical advance for manufacturing and assistive robotics. His more recent work, "FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System" (2024, 1 citation), addresses a key bottleneck in preference-based learning: helping human labelers efficiently compare and evaluate robot trajectories. This system promises to streamline how robots learn from human feedback, making them more responsive to nuanced user values. Though early in his career, Gong’s contributions are already shaping how we design interfaces that balance human oversight with robotic autonomy. His research is particularly relevant for students and engineers working on human-centered robotics, where trust, adaptability, and seamless communication are paramount.
Research Focus
Key Achievements
Top Papers
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- 2