Yijie Zhou
Papers
1
Total Citations
5
H-Index
1
About
Yijie Zhou is a pioneering researcher at the intersection of robotics, human-robot interaction, and machine learning, with a focus on developing adaptive planning systems that incorporate human feedback. Their most-cited work, "Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback" (2021, 5 citations), addresses a critical challenge in autonomous navigation: how robots can learn safe, satisfying trajectories when human preferences are subjective and difficult to model mathematically. By framing the problem as a non-contextual bandit, Zhou introduced a novel framework that allows robots to iteratively refine their behavior based on real-time human input, balancing safety with user satisfaction. This contribution is foundational for deploying robots in crowded, dynamic environments like hospitals or public spaces. Zhou’s work exemplifies a human-centric approach to AI, bridging theoretical rigor with practical deployment. Their research has been recognized for advancing the field of interactive robot learning, and they continue to shape how autonomous systems can effectively collaborate with people in the real world.
Research Focus
Key Achievements
Top Papers
- 1Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback5 citations · 2021