Siqi Tan
Papers
1
Total Citations
4
H-Index
1
About
Siqi Tan is a pioneering researcher in embodied AI and 3D perception, whose work bridges the gap between common-sense reasoning and multi-sensory robotic understanding. Their most notable contribution, the FusionSense framework (2025), revolutionizes sparse-view 3D reconstruction by enabling robots to integrate priors from foundation models with limited visual and tactile observations—mirroring how humans effortlessly combine common knowledge with sensory input. This breakthrough addresses a critical challenge in robotics: achieving robust scene understanding from minimal data. With 4 citations already in its first year, FusionSense is rapidly gaining traction as a foundational approach for efficient robotic perception. Tan’s research sits at the intersection of computer vision, robotics, and cognitive science, developing systems that emulate human-like sensory fusion. Their work promises to advance applications from autonomous manipulation to assistive robotics, where sparse sensory data is the norm. By teaching machines to “think” like humans—blending learned priors with real-time touch and vision—Tan is shaping a future where robots can navigate and interact with the physical world with unprecedented efficiency and adaptability.
Research Focus
Key Achievements
Top Papers
- 1