Danny Ho
Papers
6
Total Citations
264
H-Index
6
About
Danny Ho is a leading researcher in autonomous mobile robotics, with a focus on deep reinforcement learning, safe navigation, and robotic perception. His most influential work, "Deep Reinforcement Learning Supervised Autonomous Exploration in Office Environments" (108 citations), introduced a novel approach to long-term planning for robot exploration, moving beyond greedy methods to enable more intelligent decision-making in unknown spaces. Ho also created HouseExpo (62 citations), a large-scale 2D indoor layout dataset that has become a critical benchmark for learning-based mobile robot algorithms, addressing the field's need for standardized experimental platforms. His contributions extend to safe navigation in uneven indoor environments (40 citations), where he developed an integrated system for traversing stairs and slopes, and to robotic manipulation with a high-efficiency grasp pose detection scheme (16 citations). Notably, his work on elevator button recognition using an OCR-RCNN model (30 citations) tackles the practical challenge of inter-floor robot navigation. With over 260 total citations, Ho's research bridges the gap between theoretical deep learning and real-world robotic deployment, making him a key figure in advancing autonomous systems for complex indoor settings.
Research Focus
Key Achievements
Top Papers
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- 3Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments40 citations · 2019
- 4A Novel OCR-RCNN for Elevator Button Recognition30 citations · 2018
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