Papers
6
Total Citations
151
H-Index
4
About
Xuan Cao is a pioneering researcher at the intersection of neuromorphic computing and autonomous robotics. Her early landmark work introduced the first fully printed, all-solid-state organic flexible artificial synapse for neuromorphic computing (2019, 103 citations), a breakthrough that enabled nonvolatile, brain-inspired computing on flexible substrates—critical for human-machine interfaces, soft robotics, and medical implants. More recently, Cao has focused on endowing autonomous robots with the ability to self-assess their own proficiency and limitations. She developed the assumption-alignment tracking (AAT) framework, which allows robots to evaluate their performance in real-time without external supervision. Her papers on robot proficiency self-assessment (2023, 19 citations) and designing robots that know their limits (2022, 19 citations) have established a new paradigm for trustworthy autonomy. Cao’s work bridges hardware innovation and algorithmic self-awareness, making her a leading voice in creating robots that are not only capable but also introspective—a crucial step toward safe, reliable autonomous systems in the real world.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking19 citations · 2023
- 3A Method for Designing Autonomous Robots that Know Their Limits19 citations · 2022
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- 5
- 6Improving Robot Proficiency Self-Assessment Via Meta-Assessment3 citations · 2023