Zhaopei Gong
Papers
2
Total Citations
34
H-Index
2
About
Zhaopei Gong is a robotics researcher specializing in locomotion planning for legged systems, particularly hexapod robots operating in challenging, unstructured environments. His work addresses a critical bottleneck in field robotics: enabling multi-legged robots to navigate terrains with sparse, discrete footholds where conventional methods fail. Gong’s major contribution lies in pioneering the use of Monte-Carlo Tree Search (MCTS) for integrated gait and foothold planning—a departure from traditional approaches that treat these tasks separately and optimize only single-step decisions. His most cited paper (2021, 28 citations) introduces a contact sequence planning framework that leverages MCTS to simultaneously select gaits and footholds, dramatically improving a robot’s ability to traverse complex, obstacle-laden landscapes. A subsequent work (2020, 6 citations) extends this concept to fault-tolerant planning, ensuring robust operation even when legs are damaged or compromised. By unifying decision-making across multiple time steps, Gong’s research has advanced the theoretical foundations of legged locomotion and offers practical pathways for deploying hexapod robots in search-and-rescue, planetary exploration, and disaster response. His work stands as a key reference for researchers tackling the intersection of motion planning, reinforcement learning, and field robotics.
Research Focus
Key Achievements
Top Papers
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