Qinglei Ji

KTH Royal Institute of Technology

Papers

7

Total Citations

104

H-Index

5

About

Qinglei Ji is a pioneering researcher in soft robotics, with a focus on the design, control, and locomotion of soft quadruped robots. Their work centers on replacing traditional rigid components with tendon-driven soft actuators and continuum actuators, enabling robots to traverse complex terrains with enhanced flexibility and safety. Ji’s major contributions include synthesizing optimal gaits using deep reinforcement learning and multi-fidelity Bayesian optimization, as well as developing closed-loop control systems and 3D-printed soft deformation sensors to improve actuation precision. Their most cited paper, “Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning” (2022, 45 citations), demonstrates a novel approach to gait generation that outperforms rigid robot designs. Ji has also advanced the field through the design and calibration of soft sensors and actuators, addressing key limitations in soft robot precision and fabrication. Their work on omnidirectional walking and 4D printing for smart manufacturing further highlights their versatility. With a growing body of influential research, Ji is shaping the future of adaptive, soft robotic systems for unstructured environments.

Research Focus

Key Achievements

5
H-Index
7
Papers
104
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning
45 citations · 2022
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: KTH Royal Institute of Technology

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago