Papers
5
Total Citations
128
H-Index
4
About
Zixi Liu is a robotics researcher whose work spans two compelling domains: human-robot interaction in clinical settings and advanced robotic manipulation. Early in their career, Liu investigated how medical professionals on labor and delivery floors respond to robotic decision support systems, producing influential findings—now cited over 100 times across two related publications—demonstrating that embodied agents can engender inappropriate levels of trust and dependence among nurses and doctors. This work raised important ethical questions about automation bias in high-stakes healthcare environments. Liu's more recent research pivots toward the physical mechanics of robotic grasping and manipulation. Their 2023 paper on stochastic friction models challenges the longstanding use of Coulomb's simplified friction model in robotics, offering more realistic alternatives for reliable grasping in unstructured environments—a contribution already garnering 17 citations. Complementing this, Liu has explored the role of tactile sensing in grasp refinement, employing reinforcement learning frameworks to determine how much sensory fidelity robotic hands truly require. Their demonstration that analytic grasp stability metrics can serve as principled reward functions represents a meaningful advance in grounding reinforcement learning within well-justified physical models. With over 128 total citations, Liu's interdisciplinary contributions bridge robot cognition, physical interaction, and responsible deployment in sensitive human environments.
Research Focus
Key Achievements
Top Papers
- 1Robotic assistance in the coordination of patient care73 citations · 2018
- 2Robotic Assistance in Coordination of Patient Care27 citations · 2016
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- 5Tactile Grasp Refinement using Deep Reinforcement Learning and Analytic Grasp Stability Metrics.2 citations · 2021