Joshua D. Langsfeld
University of Maryland, College Park, Southwest Research Institute
Papers
13
Total Citations
132
H-Index
9
About
Joshua D. Langsfeld is a robotics researcher whose work sits at the intersection of robotic manipulation, machine learning, and intelligent automation for complex physical tasks. His research has made notable contributions to robotic cleaning and surface finishing of deformable and geometrically intricate objects, with a particular focus on enabling robots to adapt to unknown material properties through online learning and model estimation. Langsfeld's most influential work addresses a fundamental challenge in industrial robotics: how to automate non-repetitive tasks where part characteristics are uncertain. His series of papers on bimanual robotic cleaning—collectively drawing over 50 citations—demonstrates how robots can learn deformation models of compliant objects in real time, autonomously selecting cleaning strategies without requiring prior knowledge of material stiffness. Complementing this, his research on minimizing physical experiments for optimal trajectory identification offers practical frameworks for reducing costly trial-and-error in process applications like grinding and polishing. Beyond manipulation, Langsfeld has contributed to robot imitation learning through the SMILE simulation platform and explored human-robot collaboration to resolve perception failures in bin-picking tasks. Across his body of work, he consistently bridges theoretical modeling with real-world applicability, making his research particularly relevant for students and engineers seeking to advance automation in unstructured manufacturing environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2A virtual demonstrator environment for robot imitation learning14 citations · 2015
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- 4Robotic Finishing of Interior Regions of Geometrically Complex Parts13 citations · 2018
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