Francisco Alonso
Papers
1
Total Citations
6
H-Index
1
About
Francisco Alonso is a researcher at the intersection of human-robot interaction and machine learning, with a primary focus on active learning for social robots. His work addresses a critical challenge in robotics: how robots can efficiently learn from human guidance by intelligently selecting which features or queries to ask. His most-cited paper, "Analyzing the Impact of Different Feature Queries in Active Learning for Social Robots" (2017, 6 citations), systematically explores how varying query strategies affect a robot’s learning performance in social contexts. This contribution is foundational for designing more intuitive and adaptive robots that can engage with humans in natural, interactive learning scenarios. By optimizing the query selection process, Alonso’s research helps reduce the cognitive load on human teachers while accelerating robot skill acquisition. His work is particularly relevant for applications in education, assistive technology, and collaborative robotics. Though early in his career, Alonso’s targeted investigations into active learning strategies are shaping how robots become more effective, autonomous learners in dynamic social environments.
Research Focus
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Top Papers
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