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About

Cheston Tan is a researcher at the forefront of robotic perception and manipulation, with a focus on enabling robots to interact intelligently with their environments. His key research areas include 6D object pose estimation, learning-based dynamics models, and reinforcement learning for robotic task generalization. Tan’s major contributions lie in advancing RGB-D fusion techniques for robust 6D pose estimation under challenging conditions like heavy occlusion and poor illumination, as demonstrated in his highly cited work "6D Pose Estimation with Correlation Fusion" (2019, 2021), which has garnered a combined 18 citations. He has also pioneered efficient methods for robotic task generalization through deep model fusion reinforcement learning, reducing the extensive training typically required for adapting learned models to new environments. His review of learning-based dynamics models for robotic manipulation (2025, 9 citations) provides a comprehensive framework for predicting physical interactions, essential for planning and control. Additionally, Tan’s work on physical interaction prediction via mental simulation (2022) showcases his innovative approach to bridging perception and action. With a growing citation impact and a clear trajectory toward solving real-world robotic challenges, Cheston Tan is a rising voice in the robotics community, inspiring students and researchers alike to push the boundaries of autonomous manipulation.