Barbara Frank
Papers
12
Total Citations
307
H-Index
9
About
Barbara Frank’s research sits at the intersection of robotics, manipulation, and deformable object interaction, addressing a fundamental challenge: how can robots safely and intelligently handle environments filled with non-rigid materials like curtains, plants, or soft tissues? Her most influential work, “Learning the elasticity parameters of deformable objects with a manipulation robot” (75 citations), pioneered methods for robots to physically probe surfaces and learn their material properties—a critical step toward adaptive, real-world manipulation. She further advanced autonomous assistive robotics with her work on a robotic drinking assistant (68 citations), designed to help individuals with paralysis regain independence in basic daily tasks. Frank’s core contribution lies in integrating learning and motion planning: she developed probabilistic roadmap approaches combined with Gaussian process regression to enable mobile manipulators to navigate and plan motions in environments where objects deform upon contact. Her work on learning object deformation models (41 citations) and efficient path planning among deformable obstacles (14+ citations) has been foundational for robots operating in unstructured, human-centered spaces. By bridging perception, learning, and control, Frank has helped lay the groundwork for robots that can safely coexist and collaborate with humans in homes, hospitals, and care facilities.
Research Focus
Key Achievements
Top Papers
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- 2An autonomous robotic assistant for drinking68 citations · 2015
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- 4Learning object deformation models for robot motion planning41 citations · 2014
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- 7Real-world robot navigation amongst deformable obstacles14 citations · 2009
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