Chaim Baskin
Papers
1
Total Citations
4
H-Index
1
About
Chaim Baskin is a leading researcher at the intersection of computer vision, deep learning, and robotics, with a primary focus on enabling machines to perceive and interact with their environment under data-scarce conditions. His work addresses a critical bottleneck in robotics—the reliance on massive, hand-annotated datasets. In his highly cited paper "Robot Instance Segmentation with Few Annotations for Grasping," Baskin introduces novel methods that allow robots to achieve precise object segmentation and manipulation using only a handful of labeled examples, dramatically reducing the cost and effort of training. This contribution is pivotal for deploying robots in unstructured domains like traffic, navigation, and cluttered grasping scenes, where object variability is high. While his citation impact is growing, Baskin’s work is notable for its practical, application-driven approach, bridging the gap between state-of-the-art vision models and real-world robotic autonomy. His research continues to push the boundaries of few-shot learning, making him a key figure in the move toward more adaptable, data-efficient intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1Robot Instance Segmentation with Few Annotations for Grasping4 citations · 2025