Haoyu Mao
Papers
2
Total Citations
27
H-Index
2
About
Haoyu Mao is a rising researcher in the field of legged robotics, with a primary focus on gait planning and control for both quadruped and bipedal platforms. His work addresses the critical challenge of enabling stable, efficient locomotion in complex mechanical systems. Mao’s most impactful contribution is a hierarchical framework for quadruped robot gait planning that leverages Deep Deterministic Policy Gradient (DDPG), a deep reinforcement learning algorithm. This work, which has garnered 15 citations, tackles the difficulty of controlling robots with continuous state and action spaces, offering a scalable solution for optimal control. In parallel, Mao has advanced bipedal robotics through the kinematic analysis and gait simulation of a novel hybrid mechanical leg. His 2023 paper on this topic, with 12 citations, provides foundational models for flat-ground walking, bridging mechanical design with practical locomotion planning. By integrating reinforcement learning with traditional kinematic modeling, Mao is at the forefront of creating more adaptive and robust walking robots, making his research essential reading for students and engineers interested in the future of autonomous legged systems.
Research Focus
Key Achievements
Top Papers
- 1A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG15 citations · 2023
- 2