Papers
7
Total Citations
109
H-Index
5
About
Anqi Pan is a researcher whose work bridges the critical gap between physical sensing and intelligent decision-making in robotics and optimization. Her primary research areas encompass tactile sensing for medical robotics, reinforcement learning for skill transfer, and constrained multi-objective optimization. Pan’s most impactful contribution is her work on 3-axis tactile sensing for tissue hard-inclusion localization, which addresses the critical lack of multi-dimensional force feedback in robot-assisted minimally invasive surgery. This paper, with 54 citations, proposes a fiber-based method for comprehensive high-fidelity perception of tissue-instrument interactions. Beyond sensing, Pan has advanced reinforcement learning by developing methods for additional planning with multiple objectives and for transferring optimal contact skills to flexible manipulators. In optimization, she has introduced innovative approaches for constrained evolutionary optimization through dynamic knowledge transfer and for leveraging constraint landscape knowledge. Her work on robust performance evaluation for solution preservation in multiobjective optimization further demonstrates her commitment to addressing complex real-world problems. With a growing citation record and a portfolio that spans from hardware-level sensing to algorithmic intelligence, Anqi Pan is making notable contributions to the future of autonomous and interactive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Additional planning with multiple objectives for reinforcement learning22 citations · 2019
- 3Constrained evolutionary optimization based on dynamic knowledge transfer12 citations · 2023
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