Jing Zhang

Jing Zhang is a researcher at the Department of Signals and Systems, School of Electrical Engineering, Chalmers University of Technology. Their work focuses on robotics, machine learning, and computational science, particularly in solving long-horizon tasks using hierarchical reinforcement learning and symbolic planning.

Research interests include

• Robotics and automation
• Machine learning algorithms
• Control systems design

In 2024, Jing Zhang co-authored a paper titled Hierarchical Reinforcement Learning Based on Planning Operators, which was presented at the 20th IEEE International Conference on Automation Science and Engineering in Bari, Italy. This work introduced a novel framework integrating hierarchical reinforcement learning with symbolic planning operators, achieving a 97.2% success rate in complex robotic tasks like stacking and cube insertion. The approach reduced training time by 68%.