Haoxiang Jin
Papers
1
Total Citations
3
H-Index
1
About
Haoxiang Jin is a researcher focused on advancing human-computer interaction through reliable, interpretable planning systems. His primary research areas include behavior tree (BT) planning, knowledge-based reasoning, and human-robot collaboration. Jin’s major contribution is the development of RBT-HCI, a novel method that generates behavior trees grounded in a knowledge base, ensuring both reliability and human interpretability. This work addresses a critical gap in autonomous systems by making AI-driven decisions more transparent and acceptable to human users. While his most-cited paper currently holds 3 citations, its significance lies in laying the groundwork for safer, more trustworthy human-robot interaction. Jin’s approach emphasizes practical deployment, where behavior trees can be verified and adjusted through human feedback, bridging the gap between automated planning and user trust. His research is particularly relevant for applications in assistive robotics, smart manufacturing, and interactive AI systems. By prioritizing interpretability alongside performance, Jin is contributing to the next generation of human-aware autonomous agents that can operate effectively in shared environments.
Research Focus
Key Achievements
Top Papers
- 1