Zishang Ji
Papers
2
Total Citations
7
H-Index
2
About
Zishang Ji is a robotics researcher focused on bio-inspired locomotion, particularly the development and control of brachiation robots that mimic the swinging movement of primates. His major contributions lie in advancing control strategies for underactuated, multi-link robotic systems operating in discontinuous environments. In his most-cited work, Ji introduced a deep reinforcement learning control method for a four-link brachiation robot, achieving adaptive, point-contact locomotion that outperforms traditional control approaches. He further contributed an energy-minimization framework for trajectory generation and tracking, optimizing the robot’s swing between bars while reducing power consumption. Though early in his career, Ji’s work has already garnered attention, with his top paper accumulating 5 citations and his second paper 2 citations, reflecting growing interest in efficient, learning-based control for agile robots. His research bridges reinforcement learning and classical optimization, offering promising pathways for robots operating in complex, unstructured environments like forests or disaster zones. Ji’s innovative approach to brachiation control positions him as an emerging voice in bio-robotics and autonomous locomotion.
Research Focus
Key Achievements
Top Papers
- 1
- 2