Shouren Mao — accepted AI-conference papers

2024

RAM-NAS: Resource-aware Multiobjective Neural Architecture Search Method for Robot Vision Tasks

Shouren Mao, Minghao Qin, Wei Dong, Huajian Liu, Yongzhuo Gao

IROS 2024poster

Neural architecture search (NAS) has shown great promise in automatically designing lightweight models. However, conventional approaches are insufficient in training the supernet and pay little attention to actual robot hardware resources. To meet such challenges, we propose RAM-NAS, a resource-awar…

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2024

Sample-Efficient Learning-Based Dynamic Environment Navigation With Transferring Experience From Optimization-Based Planner

Huajian Liu, Wei Dong, Shouren Mao, Chao Wang, Yongzhuo Gao

RA-L 2024

This letter presents a sample-efficient deep reinforcement learning (DRL) based method to address the intricate task of autonomous navigation in dynamic, constrained environments. The proposed method leverages a graph neural network with feature-wise linear modulation modules to effectively extract

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