Haoran Zou
Papers
2
Total Citations
12
H-Index
2
About
Haoran Zou is a robotics researcher focused on advancing autonomous manipulation and skill learning for complex industrial tasks. His work tackles the fundamental challenge of enabling robots to perform contact-rich manipulation with high precision and safety. In his highly cited 2022 paper on robotic manipulation planning for automatic glass substrate peeling, Zou developed an online learning model predictive path integral approach to address the delicate process of peeling LCD display panels—a task demanding extreme care to avoid breakage. This work has garnered 8 citations, reflecting its practical significance for manufacturing automation. Zou also contributes to reinforcement learning for robotics, as demonstrated in his 2021 paper on efficient robot skills learning using weighted near-optimal experiences policy optimization. This research explores how policy gradient methods can enable more natural, human-like learning of robotic skills, moving beyond engineered solutions toward autonomous adaptation. With a focus on bridging the gap between theoretical reinforcement learning and real-world robotic applications, Zou’s work offers valuable insights for researchers seeking to develop safer, more efficient autonomous systems in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
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