Manuela Uliano
Papers
3
Total Citations
7
H-Index
2
About
Manuela Uliano investigates how humans intuitively manipulate objects of varying deformability and how those strategies can be transferred to robotic systems. Her research bridges human motor control and robot learning, with a focus on deformable object manipulation, bimanual teleoperation, and embodied hand motion transfer. In her most-cited work (2024, 3 citations), she systematically analyzed how humans adapt their grasping and insertion strategies when handling rigid versus deformable objects under different task constraints—a foundational step for designing more dexterous and human-aware robots. She also developed a modular approach (2022, 2 citations) that uses human demonstrations to coordinate both finger motion and end-effector pose, offering an intuitive, data-efficient method for teaching robotic hands. Additionally, her revision of the CollisionIK algorithm (2022, 2 citations) enables real-time self-collision avoidance during bimanual teleoperation, addressing a critical safety challenge without introducing delays. Though early in her career, Uliano’s work is already shaping how robots can learn from and safely interact with humans in complex, real-world manipulation tasks.
Research Focus
Key Achievements
Top Papers
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