Tianlin Liu
Papers
4
Total Citations
16
H-Index
3
About
Tianlin Liu is a researcher at the intersection of human-robot interaction (HRI) and robotic musicianship, whose work explores how robots can perceive, respond to, and learn from human behavior. His key research areas include engagement prediction in HRI, robot learning for musical performance, and autonomous mobile robot control. Liu’s most cited work, “Predicting Engagement Breakdown in HRI Using Thin-Slices of Facial Expressions” (2018, 7 citations), introduces a novel method for detecting when a human prematurely ends an interaction—a critical challenge for designing socially aware robots that sustain long-term engagement. In robotic musicianship, Liu has developed systems that enable robots to learn drumming through an open-ended internal model and a listening-playing loop, moving beyond pre-programmed routines toward adaptive, skill-based performance. His work on efficient and smooth target chasing for wheeled robots further demonstrates his breadth in autonomous control. Though early in his career, Liu’s contributions are shaping how robots interpret subtle human cues and acquire complex motor skills, laying groundwork for more intuitive and responsive robotic partners.
Research Focus
Key Achievements
Top Papers
- 1
- 2Developing Robot Drumming Skill with Listening-Playing Loop4 citations · 2017
- 3Robot Learning to Play Drums with an Open-Ended Internal Model3 citations · 2018
- 4Learning to chase a ball efficiently and smoothly for a wheeled robot2 citations · 2017