Papers
19
Total Citations
330
H-Index
11
About
Caixia Cai is a robotics and machine learning researcher whose work spans visual servoing, tactile sensing, neuromorphic computing, and robot-assisted surgery. Her research has made significant strides in advancing autonomous robotic systems, particularly in environments where traditional calibration and sensing approaches fall short. Cai is perhaps best known for her pioneering contributions to uncalibrated visual servoing, developing novel image features and stereo camera frameworks that enable precise 6-DOF manipulator control without relying on pre-calibrated systems — work that has garnered over 50 citations. Her tactile identification research, exploring hybrid touch-and-slide sensing approaches to empower robots with texture recognition capabilities, has attracted nearly 50 citations and represents a meaningful step toward more perceptive robotic systems. A distinctive thread in her portfolio is the application of spiking neural networks (SNNs) and reward-modulated spike-timing-dependent plasticity (R-STDP) for robot control tasks, including target tracking in snake-like robots and autonomous vehicle navigation — reflecting her interest in biologically inspired, energy-efficient AI. Her contributions also extend to the delicate domain of robot-assisted vitreoretinal surgery, where she developed vision-based needle pose estimation to enhance surgical precision and safety. Collectively, Cai's body of work demonstrates a rare versatility, bridging low-level sensory processing with high-level cognitive robotic frameworks.
Research Focus
Key Achievements
Top Papers
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- 46DOF Needle Pose Estimation for Robot-Assisted Vitreoretinal Surgery27 citations · 2019
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- 9Object detection using boundary representations of primitive shapes14 citations · 2015
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