Papers
23
Total Citations
342
H-Index
8
About
Qifeng Zhang is a versatile robotics and human-machine interface researcher whose work spans mobile manipulation, legged robotics, underwater systems, and brain-computer interfaces. His most influential contribution, "Learning Mobile Manipulation through Deep Reinforcement Learning" (2020, 96 citations), established foundational methods for coordinating mobile bases with robotic manipulators — a notoriously complex challenge in robotics. Zhang has consistently pushed boundaries in embodied intelligence, developing transferable frameworks for legged mobile manipulation and pioneering adaptive control strategies for quadruped robots operating across terrestrial and amphibious environments. His interdisciplinary reach extends well beyond locomotion: his work on knotted artificial muscles for deepwater actuation (64 citations) demonstrates expertise in bio-inspired soft robotics, while his survey on deep learning models for SSVEP-based brain-computer interfaces (48 citations) reflects a commitment to human-robot communication. Complementing these efforts, Zhang has contributed to underwater stereo vision systems and ROV umbilical cable dynamics, revealing a sustained focus on marine robotics. Across more than 290 cumulative citations, his research consistently bridges the gap between biological inspiration and real-world robotic deployment, making his work highly relevant for researchers in autonomous systems, rehabilitation technology, and ocean engineering.
Research Focus
Key Achievements
Top Papers
- 1Learning Mobile Manipulation through Deep Reinforcement Learning96 citations · 2020
- 2Knotted Artificial Muscles for Bio‐Mimetic Actuation under Deepwater64 citations · 2024
- 3An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey48 citations · 2023
- 4
- 5Research and Experiment of an Underwater Stereo Vision System16 citations · 2019
- 6
- 7
- 8
- 9
- 10