Qi-Zhi Cai

Papers

Total Citations

109

H-Index

About

Qi-Zhi Cai is a researcher whose work sits at the intersection of robotics, computer vision, and autonomous systems, with a particular focus on making deep learning more interpretable and robust for real-world deployment. His most prominent contribution is the development of deep object-centric policies for autonomous driving, a paradigm that challenges purely end-to-end visuomotor learning. Cai demonstrated that by explicitly representing objects within neural network architectures, autonomous agents can achieve superior generalization to novel scenes while also providing intuitive visualizations of their decision-making process—a critical step toward trustworthy AI in safety-critical applications. His foundational paper on this topic, published in 2019, has garnered over 103 citations, reflecting its influence on the field. This work addresses a fundamental tension in modern robotics: the trade-off between the appeal of end-to-end learning and the need for interpretability and robustness. By championing object-centric representations, Cai has helped pave the way for more transparent and reliable autonomous systems, making his research essential reading for anyone interested in bridging the gap between deep learning and practical robotics.