Rocio Gomez
Papers
2
Total Citations
25
H-Index
2
About
Rocio Gomez investigates the intersection of artificial intelligence, robotics, and cognitive science, with a primary focus on enabling autonomous agents to reason and act intelligently in dynamic environments. Her core research areas include affordance learning, intention recognition, and human-robot collaboration, where she develops formal frameworks for machines to understand what actions are possible and desirable. In her most cited work, "What Can I Not Do? Towards an Architecture for Reasoning about and Learning Affordances" (2017, 18 citations), Gomez introduces a novel architecture using Answer Set Prolog to represent and reason with incomplete domain knowledge, defining affordances as relations over objects and actions. This foundational contribution provides a declarative approach for agents to learn and reason about their capabilities. Her subsequent paper, "What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration" (2020, 7 citations), advances a theory of intentions grounded in non-procrastination, persistence, and relevance-limited reasoning, enabling robots to collaborate effectively with humans by inferring and acting upon shared goals. Gomez's work bridges logical reasoning and practical robotics, offering elegant solutions for agents to navigate complex, real-world tasks.
Research Focus
Key Achievements
Top Papers
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