Papers
11
Total Citations
234
H-Index
6
About
Jiucai Zhang is a leading researcher at the intersection of autonomous driving, robotic manipulation, and intelligent energy systems. His most impactful work applies deep reinforcement learning to automated lane change strategies—his 2020 paper on Proximal Policy Optimization-based lane changing has garnered over 120 citations, addressing a critical safety challenge in autonomous driving. Zhang also pioneered dynamic reconfigurable multi-cell battery architectures, a novel approach to maximizing battery performance in electric vehicles and robotics that has accumulated 50 citations. In surgical robotics, he developed attention-aware and gaze-contingent control systems for robotic laparoscope holders, enhancing human-robot cooperation in minimally invasive surgery. More recently, Zhang has advanced dexterous manipulation with multi-phase, multi-objective reinforcement learning frameworks and intuitive shared-control strategies for telemanipulation. His work on adaptive hierarchical curriculum learning and multi-agent finger cooperation represents cutting-edge approaches to in-hand manipulation. Across his career, Zhang’s research consistently bridges theoretical advances in reinforcement learning and control with practical applications in autonomous vehicles, surgical assistance, and robotic dexterity, making him a notable figure in embodied AI and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 4Attention-aware robotic laparoscope for human-robot cooperative surgery13 citations · 2013
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- 6Intent-based Task-Oriented Shared Control for Intuitive Telemanipulation7 citations · 2024
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- 8Gaze Contingent Control for a Robotic Laparoscope Holder5 citations · 2013
- 9Forming Real-World Human-Robot Cooperation for Tasks With General Goal4 citations · 2021
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