Papers
3
Total Citations
25
H-Index
2
About
Alex Vásquez is a roboticist whose work centers on **tactile perception and dexterous manipulation**, with a specific focus on enabling robotic hands to identify objects through touch alone. His key research area is **proprioceptive object recognition**, a technique that uses internal joint angles and torques—rather than external tactile sensors—to infer an object's shape during grasping. Vásquez’s major contribution is the development of **invariant proprioceptive signatures**, a method that allows a robot to recognize an object’s shape regardless of how it is held. This approach, detailed in his most-cited paper (12 citations), overcomes the limitations of prior work that could only distinguish a small set of similar objects. He further advanced the field by introducing a **collection of neural forests** for sequential shape recognition, enabling robots to identify a broader range of objects in realistic environments. Though his citation counts are modest, Vásquez’s work is notable for its **practical, data-driven approach** to a fundamental problem in robotics: how to give machines a sense of touch. His research has direct implications for prosthetics, industrial automation, and assistive robotics, where robust object identification is critical for safe and effective manipulation.
Research Focus
Key Achievements
Top Papers
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