Papers
3
Total Citations
23
H-Index
3
About
Tiantian Hao is a robotics researcher focused on advancing automated manufacturing through intelligent manipulation and locomotion. Her work centers on three key areas: visual servoing for precision grasping, reinforcement learning for energy-efficient locomotion, and trajectory imitation with compliance control. Hao’s most cited paper, “Robotic grasping and assembly of screws based on visual servoing using point features” (2023, 15 citations), introduces a novel method for screw handling that integrates real-time visual feedback to improve accuracy in assembly tasks. She further extends this work in “Trajectory Imitation With Visual Guidance and Compliance Control for Robotic Bolt Grasping and Assembly” (2024, 3 citations), proposing a three-stage framework that combines learning from demonstration with adaptive control for bolt manipulation—a significant contribution to automated manufacturing. In locomotion, her paper “Quadrupedal Locomotion in an Energy-efficient Way Based on Reinforcement Learning” (2024, 5 citations) demonstrates how RL can optimize gait patterns to reduce power consumption, with potential applications in field robotics. With a growing citation record and a focus on bridging perception, learning, and control, Hao’s research is paving the way for more dexterous and autonomous industrial robots.
Research Focus
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Top Papers
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