Elena V. Seliverstova
Papers
4
Total Citations
19
H-Index
3
About
Elena V. Seliverstova is a robotics researcher specializing in intelligent manipulation, computer vision, and autonomous systems for complex, real-world tasks. Her work focuses on enabling robots to handle deformable and hazardous objects, bridging the gap between simulation and practical deployment. She is best known for developing "Unreal Mask," a one-shot, multi-object pose estimation framework that leverages synthetic datasets and keypoints for robotic manipulation—a contribution cited 8 times for its efficiency in reducing real-world training data. Seliverstova also led the "Coinbot" project, which applied deep reinforcement learning to automate the physically demanding and safety-critical task of moving heavy coin bags in bank cash centers, earning 6 citations for its industrial impact. Her earlier research includes algorithms for planning grasps of deformable objects by multi-finger grippers, specifically targeting explosive ordnance disposal to minimize human risk. With a portfolio that integrates simulation, planning, and experimental validation, Seliverstova’s work advances the frontier of safe, autonomous manipulation in collaborative and hazardous environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4