Ana Lucia Pais Ureche
Papers
4
Total Citations
144
H-Index
4
About
Ana Lucia Pais Ureche is a roboticist advancing the frontier of **learning from demonstration (LfD)** and **hierarchical task learning**. Her research focuses on enabling robots to acquire complex, multi-step skills by observing human teachers, moving beyond simple mimicry to true task understanding. Her most influential work, "Task Parameterization Using Continuous Constraints Extracted From Human Demonstrations" (68 citations), introduces a method for automatically learning task specifications—the key variables and constraints that define a successful action—directly from human examples. This allows robots to generalize learned actions to new situations. She further extended this into the domain of sequential manipulation with her work on a pizza dough rolling case study (35 and 23 citations), where she developed a hierarchical framework that automatically segments a complex task into discrete action primitives and learns their correct sequence. This minimizes the need for human programming, making robot teaching more intuitive. Ureche is also a proponent of **open science in robotics**, co-authoring work on using web-based knowledge services to foster a more collaborative and data-driven research ecosystem. Her contributions are foundational to building robots that can learn flexible, real-world skills from non-expert users.
Research Focus
Key Achievements
Top Papers
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- 4Open robotics research using web-based knowledge services18 citations · 2016