Yiebo Chong

About

Yiebo Chong is a researcher at the forefront of artificial intelligence, specializing in visual navigation and reinforcement learning. His work focuses on enabling autonomous agents to navigate complex environments through advanced deep neural networks and multi-goal learning strategies. In his most-cited paper, "Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning" (2021, 13 citations), Chong introduces a novel framework that decouples navigation into distinct stages—first learning visual representations, then applying multi-goal reinforcement learning to achieve flexible, goal-directed movement. This approach addresses a critical challenge in robotics and AI: how to efficiently train agents to navigate toward multiple targets without retraining from scratch. By integrating hierarchical learning with deep visual processing, Chong’s work offers a scalable solution for real-world applications like autonomous drones and service robots. Though early in his career, his contributions are already shaping the next generation of intelligent navigation systems, demonstrating a clear ability to bridge theoretical advances with practical, deployable algorithms. His research continues to inspire students and engineers exploring the intersection of computer vision and decision-making.

Research Focus

Key Achievements

Top Papers

1. 1
   Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning

   13 citations · 2021

Key Collaborators

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