Papers
3
Total Citations
14
H-Index
3
About
Tiantian Yuan is a robotics researcher whose work spans energy-efficient industrial automation, reconfigurable locomotion, and inclusive human-robot interaction. Her most-cited paper, "A Data-Driven Method for Predicting and Optimizing Industrial Robot Energy Consumption Under Unknown Load Conditions" (2024, 7 citations), tackles a critical challenge in modern manufacturing: predicting energy use when load conditions are unknown. By moving beyond standard multi-layer perception models, Yuan's approach enables more adaptive and efficient energy management for diverse industrial robot fleets. In "Design and Control of a Reconfigurable Robot with Rolling and Flying Locomotion" (2024, 4 citations), she addresses the growing need for versatile robots that can seamlessly transition between aerial and terrestrial modes, designing a novel reconfigurable airframe to handle complex, multi-domain missions. Yuan also contributes to socially aware robotics through "SLRFormer: Continuous Sign Language Recognition Based on Vision Transformer" (2022, 3 citations), which applies vision transformers to continuous sign language recognition—a vital step toward making human-robot interaction accessible to deaf-mute individuals. Her work demonstrates a commitment to both technical innovation and inclusive design, positioning her as a rising voice in adaptive robotics and human-centered automation.
Research Focus
Key Achievements
Top Papers
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