Feifan Liu
Papers
1
Total Citations
6
H-Index
1
About
Feifan Liu is a rising researcher at the forefront of safe and autonomous robot task planning, with a particular focus on integrating large pre-trained models into robotic systems. His most-cited work, "Safe Planner: Empowering Safety Awareness in Large Pre-Trained Models for Robot Task Planning" (2025, 6 citations), addresses a critical gap in the field: while large models demonstrate impressive planning abilities for long-horizon tasks, they often lack inherent safety awareness. Liu’s key contribution lies in developing frameworks that embed safety constraints directly into the planning process, enabling robots to autonomously navigate complex, real-world environments without compromising on risk avoidance. This work has quickly gained traction, reflecting the urgent need for reliable AI-driven robotics. Beyond this, Liu’s research explores how to harness the reasoning power of pre-trained models while ensuring their outputs are grounded in physical-world safety—a challenge that sits at the intersection of artificial intelligence, robotics, and human-robot interaction. His early-career impact signals a promising trajectory, positioning him as a key voice in making autonomous systems both capable and trustworthy.
Research Focus
Key Achievements
Top Papers
- 1