Papers
1
Total Citations
2
H-Index
1
About
Shiyi You is a rising researcher at the intersection of robotics, human-robot interaction, and reinforcement learning, with a focus on developing intelligent systems that seamlessly collaborate with humans. Their most-cited work, "Adaptive Arbitration for Minimal Intervention Shared Control via Deep Reinforcement Learning" (2021), introduces a novel framework that uses deep reinforcement learning to dynamically adjust the balance of control between a human operator and an autonomous robot. This approach ensures the robot intervenes only when necessary, enhancing task performance while respecting human autonomy—a critical advance for applications in assistive robotics, teleoperation, and autonomous driving. Though early in their career, with 2 citations on this paper, You’s contribution addresses a fundamental challenge in shared control: how to arbitrate authority adaptively without overstepping. By combining reinforcement learning with minimal intervention principles, You’s work lays the groundwork for more intuitive and efficient human-robot teams, promising safer and more effective collaboration in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1