Cristina Cornelio

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About

Cristina Cornelio is an emerging researcher at the intersection of artificial intelligence, neuro-symbolic computing, and robotics. Her work focuses on developing hybrid frameworks that combine the reasoning capabilities of large language models with symbolic methods to address real-world challenges in autonomous systems. Her most notable contribution, the RECOVER framework (2024), tackles one of robotics' persistent challenges: enabling robots to detect when tasks go wrong and autonomously recover from failures — a capability critical for deploying reliable robotic systems in dynamic, real-world environments. By bridging neural and symbolic approaches, Cornelio's research moves beyond the limitations of purely data-driven or constraint-based methods, leveraging the flexibility of large language models while maintaining structured reasoning. With 11 citations already accumulated for this 2024 work, her research is gaining rapid traction within the robotics and AI communities. Cornelio's contributions are particularly significant for researchers working on autonomous task planning, human-robot interaction, and trustworthy AI systems, positioning her as a promising voice in the growing field of neuro-symbolic AI applied to embodied intelligence.