Thais G. Do Rego
Papers
1
Total Citations
2
H-Index
1
About
Thais G. Do Rego is a roboticist whose research lies at the intersection of computer vision and manipulation, focusing on how robots perceive and interact with unstructured environments. Her most-cited work, "Evaluating Data Representations for Object Recognition During Pick-and-Place Manipulation Tasks" (2022), tackles a fundamental challenge in autonomous robotics: enabling a robot to simultaneously build both local and global descriptions of its surroundings. This dual representation is critical for tasks like object recognition and pose estimation, which are essential for reliable pick-and-place operations. By systematically evaluating different data representations, Do Rego’s work provides a practical framework for improving a robot’s ability to identify and grasp objects in cluttered, real-world settings—a key step toward more adaptive and autonomous industrial and service robots. Though early in her career, her contributions are already shaping how researchers approach the integration of perception and manipulation, with her work cited by peers seeking robust solutions for robotic dexterity. Her research promises to bridge the gap between raw sensor data and actionable robotic intelligence.
Research Focus
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Top Papers
- 1