Shreshtha Basu
Papers
1
Total Citations
2
H-Index
1
About
Shreshtha Basu is a roboticist whose research lies at the intersection of perception, manipulation, and affordance learning. Her work focuses on enabling robots to generalize manipulation actions to novel objects in cluttered, unstructured environments—a critical step toward truly autonomous systems. In her highly cited 2022 paper, *Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames*, Basu introduces a novel framework that allows robots to recognize functional object properties—such as which novel container can be grasped to pour a drink—without prior exposure to that specific instance. By grounding perception in task-relevant coordinate frames, her approach bridges the gap between object recognition and actionable understanding, enabling robust operation even in messy, real-world settings. While still early in her career, her work has already garnered attention for its practical, manipulation-oriented perspective on affordance learning. Basu’s contributions are paving the way for robots that can adapt to new environments and tasks with minimal pre-programming—a vision that promises to make household and service robots far more capable and intuitive in the years ahead.
Research Focus
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Top Papers
- 1