Papers
10
Total Citations
165
H-Index
7
About
Fan Feng is a researcher at the forefront of lifelong learning and robotic vision, with a focus on enabling robots to continuously adapt to real-world environments. His most significant contribution is the development of the **OpenLORIS-Object** dataset and benchmark, a pioneering resource designed to address the unique challenges robotic vision systems face compared to standard computer vision tasks — particularly the need to learn incrementally without forgetting previously acquired knowledge. This work, which has accumulated over 70 citations across multiple versions, has become a key reference for researchers working on continual and lifelong deep learning. Feng has also made meaningful contributions to task incremental learning for assistive robotics (44 citations) and helped organize the IROS 2019 Lifelong Robotic Vision Challenge, engaging over 150 international teams in advancing the field. His research extends into multimodal perception, including vision-audio integration for fluid mass estimation and container property prediction for safe human-robot handovers under the CORSMAL benchmark. With a total citation count exceeding 160, Fan Feng's work consistently bridges the gap between controlled academic benchmarks and the messy, dynamic demands of real-world robotic deployment, making him a notable emerging voice in intelligent robotics research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Challenges in Task Incremental Learning for Assistive Robotics44 citations · 2019
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- 6The CORSMAL Benchmark for the Prediction of the Properties of Containers9 citations · 2022
- 7Towards lifelong object recognition: A dataset and benchmark8 citations · 2022
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