Pin-Han Lin
Papers
1
Total Citations
7
H-Index
1
About
Pin-Han Lin is a leading researcher in autonomous robotics and artificial intelligence, specializing in decision-making under uncertainty. His primary research areas include task and motion planning, probabilistic inference, and partially observable Markov decision processes (POMDPs), with a focus on enabling robots to perform long-horizon tasks in complex, real-world environments. Lin’s most notable contribution is his 2021 paper, "Probabilistic Inference in Planning for Partially Observable Long Horizon Problems," which has garnered 7 citations and addresses a critical gap in robotics: most task and motion planning approaches assume full state observability, rendering them ineffective in stochastic settings. By integrating probabilistic inference into planning, Lin’s work provides a framework for autonomous service robots to act intelligently despite partial observability, advancing the field’s ability to handle real-world uncertainty. His research has significant implications for service robotics, where robots must navigate dynamic, unpredictable spaces. Lin’s achievements are recognized for bridging theoretical planning methods with practical deployment, making him a rising voice in robotics and AI. His work continues to inspire students and researchers tackling the challenges of autonomous systems in uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1