Andrew Guo
Papers
1
Total Citations
30
H-Index
1
About
Andrew Guo is a leading researcher in computer vision and robotics, specializing in category-level object pose estimation and affordance prediction. His most impactful work, the HANDAL dataset (2023, 30 citations), addresses a critical gap in robotic manipulation by providing high-quality pose annotations, affordances, and 3D reconstructions for real-world, manipulable objects like pliers and utensils. This dataset enables robots to functionally grasp and interact with everyday items, moving beyond traditional benchmarks focused on static or non-manipulable objects. Guo’s contributions advance the integration of perception and action, empowering robots to understand not just an object’s position but also its potential uses. His work has been recognized for its practical relevance in robotics and computer vision, bridging the gap between simulated training and real-world deployment. By focusing on robotics-ready objects, Guo’s research directly impacts autonomous systems in manufacturing, assistive robotics, and household automation, making him a key figure in developing more capable and adaptable robotic hands.
Research Focus
Key Achievements
Top Papers
- 1