Gabriela Zarzar Gandler
Papers
3
Total Citations
52
H-Index
2
About
Gabriela Zarzar Gandler is a roboticist whose research lies at the intersection of perception, machine learning, and 3D shape modeling. Her primary focus is on enabling robots to infer and represent the three-dimensional shapes of objects from noisy, incomplete sensory data—a fundamental challenge for autonomous manipulation. Her most influential work introduces a novel approach to object shape estimation using **sparse Gaussian process implicit surfaces**, which elegantly fuses visual and tactile data to build accurate models despite imperfect observations (44 citations). This contribution is critical for robots operating in unstructured environments where visual occlusion and sensor noise are common. She has also advanced the field by releasing real-world robotic datasets of visual and tactile point clouds, providing a benchmark for shape completion and modeling. Her work on probabilistic representations further demonstrates her commitment to robust, uncertainty-aware perception. By tackling the core problem of how robots can "feel" and "see" objects to understand their geometry, Zarzar Gandler is paving the way for more dexterous and perceptive robotic systems.
Research Focus
Key Achievements
Top Papers
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