Daojin Yao
Papers
7
Total Citations
98
H-Index
5
About
Daojin Yao is a leading researcher in the field of bipedal robotics and intelligent control systems, with a focus on achieving stable, energy-efficient, and human-like locomotion. His work centers on passive dynamic walking (PDW), a biologically inspired approach that leverages natural dynamics for efficient gait. Yao has made major contributions by integrating reinforcement learning, particularly deep Q-networks, to develop adaptive controllers that enhance the stability of passivity-based biped robots on compliant and uneven terrain. His 2018 paper on intelligent controllers for PDW using deep Q-networks has garnered 17 citations, while his 2021 study on velocity-based gait planning for underactuated bipedal robots on uneven ground has 25 citations. Yao’s most cited work (29 citations) from 2024 introduces a fusion of improved RRT* and TEB algorithms for mobile robot route planning, showcasing his versatility in addressing both bipedal and wheeled robot navigation. His notable achievements include experimental validation of control methods for underactuated walking on compliant ground, as well as recent work on agricultural robotics, such as a 2025 paper on deep learning-based grasping for shiitake mushroom harvest robots. Yao’s research bridges theoretical advances in control theory with practical robotic applications, making him a key figure in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Route planning of mobile robot based on improved RRT star and TEB algorithm29 citations · 2024
- 2
- 3Intelligent controller for passivity-based biped robot using deep Q network17 citations · 2018
- 4
- 5
- 6
- 7