Papers
7
Total Citations
1,090
H-Index
7
About
Yu Fan Chen is a robotics researcher whose work sits at the intersection of autonomous navigation, deep reinforcement learning, and human-robot interaction. His research primarily addresses one of the most challenging problems in mobile robotics: enabling autonomous vehicles and robots to navigate safely and efficiently in dynamic, pedestrian-rich environments. Chen's most influential contribution is his pioneering work on socially aware motion planning using deep reinforcement learning, which has garnered over 715 citations since its 2017 publication. This research tackled the nuanced challenge of encoding implicit human navigation rules — such as passing on the right — into autonomous systems, a problem deceptively simple for humans but remarkably difficult to formalize computationally. His follow-up work on collision avoidance in pedestrian-rich environments (228 citations) further refined this framework, while his 2018 paper extended these methods to handle more realistic assumptions about dynamic, decision-making agents. Beyond reinforcement learning, Chen has contributed to motion planning with diffusion maps and developed MAR-CPS, a measurable augmented reality platform for prototyping and testing cyber-physical systems. This tool bridges the gap between simulation and real-world deployment, making algorithm validation more accessible. With nearly 1,100 cumulative citations, Chen's work has meaningfully shaped the trajectory of human-aware robot navigation research.
Research Focus
Key Achievements
Top Papers
- 1Socially aware motion planning with deep reinforcement learning715 citations · 2017
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- 4Socially Aware Motion Planning with Deep Reinforcement Learning42 citations · 2017
- 5Motion planning with diffusion maps28 citations · 2016
- 6
- 7MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems12 citations · 2015