Jin-Ling Lin
Papers
10
Total Citations
209
H-Index
7
About
Jin-Ling Lin is a leading researcher in multi-robot coordination and humanoid locomotion, whose work bridges reinforcement learning, imitation learning, and autonomous patrol planning. Her most influential contributions center on developing intelligent control schemes that enable robots to operate autonomously in unstructured environments. In her highly cited 2013 work (55 citations), she introduced a hybrid formation control scheme based on weighted behavior learning, allowing robots to self-organize without complex programming. Her pioneering use of Q-learning for bipedal locomotion—demonstrated in papers with 48 and 31 citations—enabled robots to dynamically balance and refine gait patterns without prior knowledge of their dynamic models, a significant advance in humanoid robotics. Lin also made notable contributions to multi-robot patrol systems, proposing a competitive auction mechanism that allows robot teams to cooperatively plan and adapt patrol routes in real time. Her 2017 work on imitation learning further extended her impact, enabling humanoid robots to replicate human motion while maintaining balance. With a research portfolio spanning autonomous navigation, cooperative control, and adaptive locomotion, Lin’s work has shaped how robots learn to move and collaborate in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1A Simple Scheme for Formation Control Based on Weighted Behavior Learning55 citations · 2013
- 2Gait Balance and Acceleration of a Biped Robot Based on Q-Learning48 citations · 2016
- 3Learning to Adjust and Refine Gait Patterns for a Biped Robot31 citations · 2015
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- 6An ensemble method for inverse reinforcement learning16 citations · 2019
- 7Dynamic Patrol Planning in a Cooperative Multi-robot System7 citations · 2011
- 8Humanoid robot gait imitation5 citations · 2014
- 9Policy Learning with Human Reinforcement4 citations · 2016
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