Jiahao Jiang
Papers
3
Total Citations
45
H-Index
3
About
Jiahao Jiang is a rising researcher in embodied artificial intelligence, whose work focuses on enabling autonomous navigation for mobile robots in complex, dynamic environments. His primary research areas span spatial-temporal reasoning, deep reinforcement learning, and representation learning from visual inputs. Jiang’s most significant contribution is the development of the ST² (Spatial-Temporal State Transformer) framework, which empowers crowd-aware autonomous navigation by jointly modeling spatial and temporal state information. This work, published in 2023, has already garnered 33 citations, reflecting its impact on the field. He further advanced the state of the art with BEVNav, a novel approach that leverages Bird’s-Eye View representations and spatial-temporal contrastive learning to achieve reliable, map-less navigation. Additionally, his work on DMCL (Depth Image Masked Contrastive Learning) addresses the critical challenge of learning effective state representations from high-dimensional pixel inputs for reinforcement learning. Through these contributions, Jiang is helping to bridge the gap between perception and action in autonomous systems, with his research being particularly relevant for applications in service robotics, autonomous driving, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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