Tse-kai Chan
Papers
2
Total Citations
16
H-Index
2
About
Tse-kai Chan is a leading researcher in embodied AI and robot learning, with a focus on building scalable simulation frameworks that bridge the gap between virtual training and real-world deployment. His most impactful work centers on the development of ManiSkill3, a GPU-parallelized robotics simulation and rendering platform designed to enable generalizable embodied intelligence. Chan’s major contributions include engineering a simulation environment that supports a wide range of scenes and tasks—overcoming the limitations of prior frameworks—while incorporating critical features for sim-to-real transfer. The foundational paper on ManiSkill3 (2024) and its demonstration paper (2025) have together garnered 16 citations in just their first year, signaling rapid adoption by the research community. By open-sourcing this platform, Chan has provided the field with a powerful tool for compute-scalable robot learning, enabling researchers to train policies that generalize across diverse tasks. His work represents a significant step toward practical, real-world robotics, making him a key figure in the next generation of embodied AI research.
Research Focus
Key Achievements
Top Papers
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