Papers
4
Total Citations
25
H-Index
3
About
Landong Hou is a robotics researcher focused on advancing legged locomotion, particularly for quadruped and biped robots operating in complex, real-world environments. His work centers on integrating perception, planning, and control to enhance robot autonomy and human-robot interaction. Hou’s major contributions include developing a novel obstacle avoidance and personnel following strategy for quadruped robots by fusing ultra-wideband positioning with 3D LiDAR (13 citations), and proposing a deep reinforcement learning (DRL)-based model predictive controller that treats swinging legs as disturbances to a single rigid body model for bipedal robots (6 citations). He has also pioneered a vision-based dynamic gait stair climbing algorithm using depth cameras and Capture Point control (4 citations), and a DRL-based motion planning method that eliminates the need for tedious manual gait design (2 citations). Collectively, Hou’s work demonstrates a systematic approach to making legged robots more adaptive, stable, and capable in unstructured settings—pushing the boundaries of how these machines can assist humans in daily tasks and hazardous environments.
Research Focus
Key Achievements
Top Papers
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- 3Vision-based Dynamic Gait Stair Climbing Algorithm for Quadruped Robot4 citations · 2022
- 4