Papers
15
Total Citations
514
H-Index
10
About
Liting Sun is a robotics and autonomous systems researcher whose work spans human-robot interaction, robot learning, and intelligent planning. Her research addresses some of the most pressing challenges in modern robotics: enabling machines to collaborate safely and efficiently with humans in dynamic, uncertain environments. Sun's most influential contribution, "Learning Variable Impedance Control via Inverse Reinforcement Learning" (2021, 112 citations), demonstrated how robots can adaptively modulate their physical interactions with unknown environments—a critical capability for real-world manipulation tasks. Her parallel work on human-robot collaboration, including efficient plan recognition and trajectory prediction (2020, 98 citations), has significantly advanced manufacturing automation by making robots more responsive to human intent. Notably, her 2018 paper on "Courteous Autonomous Cars" (95 citations) introduced the compelling idea that autonomous vehicles should optimize not just for safety and efficiency, but for social awareness—a conceptual shift that has influenced thinking in autonomous driving. Her game-theoretic planning frameworks further reflect her commitment to modeling human cognition, including irrationality and latent intent, to build robots that are both safe and socially intelligent. Across her body of work, Sun bridges machine learning, control theory, and human factors, making her a distinctive voice in the field of human-centered robotics.
Research Focus
Key Achievements
Top Papers
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- 3Courteous Autonomous Cars95 citations · 2018
- 4Human-Aware Robot Task Planning Based on a Hierarchical Task Model55 citations · 2021
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- 9Efficient Robot Motion Planning via Sampling and Optimization14 citations · 2021
- 10Robust dexterous manipulation under object dynamics uncertainties13 citations · 2017