Papers
5
Total Citations
57
H-Index
5
About
Chenpeng Yao is a robotics researcher whose work bridges bioinspiration and algorithmic rigor to solve fundamental challenges in autonomous navigation and locomotion. His primary research areas include humanoid locomotion control, multi-agent path planning, and lidar-based odometry. Yao’s most impactful contribution is a hierarchical central pattern generator (H-CPG) model for adaptive humanoid gait planning, which modulates center-of-mass and foot trajectories to achieve robust locomotion (22 citations). He has also advanced mobile robot perception by developing a dense normal-based degeneration-aware 2-D lidar odometry method that addresses scan matching failures in underconstrained environments like corridors (16 citations). In multi-agent systems, Yao identified and relaxed critical limitations of the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, improving crowd navigation by removing improper reciprocal assumptions (9 citations). His work extends to deep reinforcement learning for obstacle avoidance in dynamic settings and side-preference path optimization for multi-agent coordination. With papers published between 2020 and 2024, Yao’s research demonstrates a consistent focus on making robots more adaptive, perceptive, and socially aware in complex real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Navigation in Multi-Agent System With Side Preference Path Optimizer5 citations · 2021