Silvia Izquierdo-Badiola
Papers
4
Total Citations
28
H-Index
2
About
Silvia Izquierdo-Badiola is a leading researcher in human-robot collaboration (HRC), focusing on adaptive task planning and AI-driven robotic reasoning. Her work bridges classical AI planning with large language models (LLMs) to create robots that understand abstract human goals and dynamic conditions—like a user with back pain asking for kitchen help. In her highly cited paper "PlanCollabNL" (2024, 15 citations), she pioneered LLM-based plan generation that adapts task allocation to human states, moving beyond rigid, predefined workflows. Her 2022 study on "Improved Task Planning through Failure Anticipation" (9 citations) introduced proactive error detection, enabling robots to foresee and avoid collaboration breakdowns. More recently, "Raider" (2025) developed an LLM-powered agent that detects, explains, and recovers from robotic action issues, enhancing system robustness. Izquierdo-Badiola’s work is distinguished by its practical focus on real-world adaptability—using evolutionary learning to adjust action costs based on human preferences and physical limitations. With a growing citation impact and multiple first-author publications in top venues, she is shaping the next generation of collaborative robots that are not just efficient but empathetic, understanding both what humans say and what they truly need.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4