Siqi Feng
Papers
2
Total Citations
17
H-Index
2
About
Siqi Feng is a rising researcher at the forefront of human-robot collaboration (HRC), with a specific focus on making industrial assembly processes more intuitive and efficient. Her work centers on the critical challenge of turn-taking prediction, aiming to enable robots to anticipate and seamlessly respond to human actions in collaborative manufacturing environments. Feng’s most-cited paper, “Turn-Taking Prediction for Human–Robot Collaborative Assembly Considering Human Uncertainty” (2023, 11 citations), addresses the inherent unpredictability of human behavior, proposing a framework that allows robots to adapt their timing and actions for smoother teamwork. Building on this, her earlier work, “Early prediction of turn-taking based on spiking neuron network to facilitate human-robot collaborative assembly” (2022, 6 citations), introduces a biologically inspired approach using spiking neural networks to achieve rapid, early-stage predictions. By tackling the core issue of coordination under uncertainty, Feng is contributing to the vision of Industry 5.0, where flexible, human-centric automation enhances both productivity and worker experience. Her research holds significant promise for the future of smart manufacturing.
Research Focus
Key Achievements
Top Papers
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- 2