Papers
7
Total Citations
87
H-Index
4
About
Hao Tian is a robotics researcher whose work spans legged locomotion, human-robot interaction, and intelligent manufacturing. His most impactful contribution is a general reinforcement learning framework for quadrupedal locomotion that integrates an evolutionary trajectory generator, addressing the challenge of reward sparsity in complex nonlinear dynamics—a paper that has earned 55 citations since 2022. Tian has also advanced safe policy optimization in navigation through his Intervention Aided Reinforcement Learning (IARL) framework, which mitigates safety and cost concerns in practical deployments. In human-robot interaction, he developed a proactive framework for social receptionist robots that uses vision-based active greeting to improve customer satisfaction. His research extends to space robotics, where he has worked on configuration planning for space cellular robotic systems, and to industrial applications, including visual servo positioning for railway wagon assembly and lightweight object detection for wedge support robots. This breadth—from foundational RL methods to applied manufacturing systems—demonstrates Tian’s ability to drive both theoretical innovation and practical deployment in robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Proactive Interaction Framework for Intelligent Social Receptionist Robots13 citations · 2021
- 3
- 4Configuration Planning of Space Cellular Robotic System4 citations · 2019
- 5Kinematics Analysis and Simulation on a Four-legs Jumping Robot4 citations · 2012
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