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About

Tiago Mota is a robotics and artificial intelligence researcher whose work sits at the intersection of commonsense reasoning, knowledge acquisition, and deep learning. His research addresses a fundamental challenge in modern AI: how to make data-hungry deep learning models more practical and trustworthy in real-world robotic systems. By integrating knowledge-based reasoning with data-driven learning, Mota has developed architectures that reduce dependence on large labeled datasets while enabling robots to make more transparent, explainable decisions — a critical requirement for effective human-robot collaboration. Among his most influential contributions is a line of work exploring how commonsense reasoning can guide deep learning in robotics, which has attracted over 22 citations and spawned multiple extensions through 2022. His research on grounding spatial relations between objects (13 citations) further demonstrates his commitment to building robots that can understand and communicate about their environment in human-friendly terms. Notably, Mota has also tackled the challenge of explainability directly, developing systems capable of generating disambiguation queries and belief traces that make robotic decision-making interpretable. With a consistent publication record across top venues and a cumulative citation impact exceeding 80, Mota represents an emerging voice in trustworthy, human-centered robotics research.