Papers
6
Total Citations
97
H-Index
5
About
Xinyue Hao is a leading researcher in lifelong robotic vision, with a focus on enabling machines to learn continuously from their environments—much like humans do. Their most significant contribution is the creation of the OpenLORIS-Object dataset and benchmark, a foundational resource for evaluating lifelong deep learning in robotic vision. This dataset addresses the critical challenge of catastrophic forgetting, where models lose previously learned knowledge when trained on new tasks. Hao’s work has been widely recognized, with their seminal 2020 paper garnering 58 citations and establishing a standard for the field. They also played a key role in organizing the IROS 2019 Lifelong Robotic Vision Challenge, which attracted over 150 teams and spurred advances in object recognition under real-world, non-stationary conditions. Through these efforts, Hao has provided the community with essential tools and benchmarks, driving progress toward truly adaptive, lifelong learning robots. Their research is indispensable for anyone working in continual learning, robotic perception, or autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Towards lifelong object recognition: A dataset and benchmark8 citations · 2022
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