Chuanlin Lan
Papers
8
Total Citations
115
H-Index
6
About
Chuanlin Lan is a leading researcher in robotic vision and lifelong machine learning, with a focus on enabling robots to continuously learn and adapt from their environments. His most impactful contribution is the creation of the **OpenLORIS-Object** dataset and benchmark, a foundational resource for lifelong deep learning in robotics that has garnered over 58 citations. This work directly addresses the critical challenge of catastrophic forgetting, where AI models lose previously learned knowledge when trained on new data. Lan also spearheaded the **IROS 2019 Lifelong Robotic Vision Challenge**, a global competition that attracted over 150 teams and established standardized evaluation protocols for lifelong object recognition. Beyond visual recognition, his research extends to multimodal perception, as demonstrated in his work on **VA2Mass** and the **CORSMAL Benchmark**, which integrate vision and audio for estimating container properties—a key safety requirement for human-to-robot handovers. With over 100 combined citations across his top publications, Lan’s contributions are shaping the future of autonomous robots that can learn continuously, perceive their surroundings robustly, and interact safely with humans.
Research Focus
Key Achievements
Top Papers
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- 5The CORSMAL Benchmark for the Prediction of the Properties of Containers9 citations · 2022
- 6Towards lifelong object recognition: A dataset and benchmark8 citations · 2022
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